dataesr/scientific-paragraphs-categorization huggingface dataset:
(2013). The method is based on the concept of inverted modelling  (Doeschl-Wilson et al. , 2007) , in which real data are compared with model outputs and model parameters are adjusted iteratively to minimize the difference between predicted and observed values. A calibration procedure based on a Broyden-Fletcher-Goldfarb-Shanno optimization method was applied to fit the equations for FI and growth (through PD) to observation with the assumption that energy or nutrients were not limitating during the test. In the model, the Gompertz function is used to model PD during growth  (van Milgen et al. , 2008). The lipid deposition (LD) is then estimated from the energy consumed and the energy required for maintenance, physical activity and PD. The BW is finally estimated from the predicted protein and lipid mass.
Within age, contrasts between least square means of feed efficiency groups were tested with a t-test accounting for multiple testing within ages. Significant results were reported for P < 0. 0167 (0. 05/3 tests). Finally, genetic parameters were estimated by extending the data set to animals tested between 2005 and 2009 in the three breeds, giving a total of 5364 LWD, 1095 LWS and 3101 LR pigs. These additional pigs had only two BW measurements, at the beginning and at the end of test (BW 1 and BW 2 ), during the test. As a consequence they had no estimates for model parameters, but they had records for all production traits recorded on related pigs raised in 2010. Variance components were therefore estimated using these additional data to provide maximum pedigree information for animals having records in 2010, and to maximize the number of performance available on ADFI, ADG, RFI and FCR.
, 2007; Cai et al. , 2008)  or different (Clutter, 2011;  Saintilan et al. , 2013) breeds. Because of its calculation, RFI is phenotypically independent from ADG, LMC, BFT and DP, and the corresponding genetic correlations are expected to be low. As a result, no significant differences were expected for these traits between RFI groups. Only the slight difference for LMC between the RFI -and RFI + groups did not support this expectation. Mean values for growth and FI model parameters were in agreement with those reported by  Brossard et al. (2006)  in pigs from a three-way cross and by  Vautier et al. (2013)  in LW × Piétrain pigs. To describe growth and FI dynamics, the InraPorc ® software estimates parameters with biological meaning that can be interpreted as production indicators for the growing-finishing period.
Proportion of cases consulting a physician Healthcare seeking behaviour for acute gastroenteritis (AG) in France was assessed in a population based survey carried out in 2009-2010 and identified duration of illness as the most important factor associated with consultation  (Van Cauteren et al. , 2012). Therefore we estimated specific proportions of laboratory confirmed campylobacteriosis or salmonellosis cases who had short (1-2 days), medium (3-5 days) and long (more than 5 days) duration of illness. We used data from a case control study for campylobacteriosis  (Gallay et al. , 2008)  and from outbreak investigations for salmonellosis (unpublished data, InVS). For the outbreak investigation data duration of diarrhoea was used as a proxy for the duration of illness. Consultation rates for short, medium and long duration of illness estimated in the population based survey were then applied to the proportion of reported cases in each category to estimate the proportion of cases of campylobacteriosis and salmonellosis consulting a physician.
Number of hospitalisations The national hospital information system (PMSI: Programme de Médicalisation du Système d'Information) was used to estimate the number of cases of campylobacteriosis or salmonellosis hospitalised for their illness. This database records administrative and medical information of all public and private hospitals in France. For each hospitalisation, discharge diagnoses are coded in the database according to the international classification of diseases (ICD-10). Age, gender, length of stay and residential location are also recorded. We selected in the PMSI all records with a discharge date between January 2008 and December 2013 containing codes (principal, related or secondary diagnoses) for campylobacteriosis (A04. 5) or salmonellosis (A02. 0 to A02. 9). Trends over time, age and gender of the PMSI records were compared to NRC surveillance data in order to assess the validity of the PMSI database to estimate the number of hospitalisations for both pathogens (data not shown).
Hospitalisations On average, between 2008 and 2013, 3,088 campylobacteriosis and 4,194 salmonellosis associated hospitalisations were annually identified in the PMSI database. After correcting for the diagnostic sensitivity of stool culture the mean annual number of hospitalizations for campylobacteriosis was estimated at  5,182 (min 4,750; max 5,614) and 4,305 (min 4,194; max 4,415)  for salmonellosis, resulting in an annual incidence rate of 8. 3 hospitalised campylobacteriosis cases/100,000 and 6. 9 hospitalised salmonellosis cases/100,000 respectively.
DISCUSSION We estimated that  528, 780 cases (90%CrI 329, 060, 616) of campylobacteriosis and 192, 450 cases (90%CrI 108, 362)  of salmonellosis occur each year in France. The multiplication factors between cases ascertained through surveillance and cases in the community were 115 for campylobacteriosis and 20 for salmonellosis. The lower proportion of stool samples tested, the lower diagnostic sensitivity, together with a lower completeness of case reporting, explain the higher multiplication factor for campylobacteriosis than for salmonellosis. Accurately estimating community incidence using a pyramid reconstruction approach is challenging and depends on data sources informing about the different parameters. Duration of illness was derived from laboratory-confirmed cases reported to the NRC and was used in the calculations to estimate the proportion of cases consulting for their illness.
Background: During the last fifteen years, cell wall proteomics has become a major research field with the publication of more than 50 articles describing plant cell wall proteomes. The WallProtDB database has been designed as a tool to facilitate the inventory, the interpretation of cell wall proteomics data and the comparisons between cell wall proteomes. Results: WallProtDB (http://www. polebio. lrsv. ups-tlse. fr/WallProtDB/) presently contains 2170 proteins and ESTs identified experimentally in 36 cell wall proteomics studies performed on 11 different plant species. Two criteria have to be met for entering WallProtDB. First one is related to the identification of proteins. Only proteins identified in plant with available genomic or ESTs data are considered to ensure unambiguous identification.
Second criterion is related to the difficulty to obtain clean cell wall fractions. Indeed, since cell walls constitute an open compartment difficult to isolate, numerous proteins predicted to be intracellular and/or having functions inside the cell have been identified in cell wall extracts. Then, except proteins predicted to be plasma membrane proteins, only proteins having a predicted signal peptide and no known intracellular retention signal are included in the database. In addition, WallProtDB contains information about the strategies used to obtain cell wall protein extracts and to identify proteins by mass spectrometry and bioinformatics. Mass spectrometry data are included when available. All the proteins of WallProtDB are linked to ProtAnnDB, another database, which contains structural and functional bioinformatics annotations of proteins as well as links to other databases (Aramemnon, CAZy, Planet, Phytozome).
The type of identified proteins and the ratio between identified proteins predicted to be secreted and identified leaderless proteins depends on the type of method used and on the type of plant material  [10, 11]. The issue of the non-canonical CWPs, i. e. proteins having no predicted signal peptide, has been a matter of debate since the first cell wall proteomics studies  [12] [13] [14]. The second problem is the quality of functional annotations of proteins in databases. They are often not sufficiently reliable to allow an appropriate biological interpretation of proteomics data because they are mostly based on sequence comparisons  [15, 16]. The third problem occurs with plants for which sequence data are not available. In this case, the proteins cannot be unambiguously identified. This is a major problem in plants since most cell wall proteins belong to multigene families  [17].
All these difficulties make the comparison between different cell wall proteomes a challenging task. In order to answer such questions, WallProtDB (http:// www. polebio. lrsv. ups-tlse. fr/WallProtDB/) was built in 2008 as a tool (i) to collect cell wall proteomics data, (ii) to facilitate their biological interpretation, and (iii) to allow comparisons between cell wall proteomes of different plant species. A new version of WallProtDB has been recently launched with new tools allowing the comparison between cell wall proteomes from different organs of the same plants or from different plants. WallProtDB contains experimental published data which are manually curated and is restricted to plants for which sequence data, genomic or ESTs, are available. Protein accession numbers are linked to another database, ProtAnnDB (http://www.
Construction and updating The construction and the updating of WallProtDB are supported by the following steps performed to ensure a reliable database (Figure  1 ):  http://prosite. expasy. org/PS00014) and no more than one trans-membrane domain as predicted by TMHMM (http://www. cbs. dtu. dk/services/ TMHMM-2. 0/). (  4 )Bioinformatics prediction of functional domains. (1) This annotation is done using ProtAnnDB (see below). (  5 )Definition of a dictionary for the functional annotation of proteins, based on Pfam (http://pfam. xfam. org) [18]  or InterPro (http://www. ebi. ac. uk/ interpro/)  [19]  domain repertoires. This step ensures that the same annotation is used for all the proteins sharing the same predicted functional domains. (6)Classification of proteins into 8 functional classes on the basis of the presence of predicted functional domains  [10] : proteins acting on cell wall polysaccharides, oxido-reductases, proteases, proteins related to lipid metabolism, proteins with interaction domains (with proteins or polysaccharides), proteins possibly involved in signaling, structural proteins, proteins with yet unknown function.
All the other proteins are included in a ninth class named "miscellaneous proteins" (Additional file 1). (  7 )Design of a flowchart form allowing the description of most of the possible strategies usable to isolate CWPs and to identify them by MS and bioinformatics  [3]. Customize the form for each set of experimental data (for an example, see Analysis of the cell wall proteome of Brachypodium distachyon young leaves: http://www. polebio. lrsv. ups-tlse. fr/ WallProtDB_data/biblio/biblio26. html). (8)Addition of MS data when available using either the X! Tandem software  [20]  or links to excel sheets found as supplementary data in articles of interest. When the data are in the X!Tandem format, it is possible to visualize the sequenced peptides on the protein sequence and their MS/MS fragmentation data.
(9)Search for homologous proteins in closely related genomes when only ESTs are available. The identification of homologous genes allows completing the bioinformatics prediction of signal peptide and/or of functional domains when EST sequences are not full-length. This is the case for Saccharum officinarum and Brassica oleracea for which homologous genes have been searched for in Sorghum bicolor and Arabidopsis thaliana respectively. (10) Addition of cell wall proteomics literature. Direct links to articles or to their abstract in PubMed-NCBI (http://www. ncbi. nlm. nih. gov/pubmed) are available. Data are stored in a mySQL database. WallProtDB is queried through a web interface constructed in the PHP code (http://www. php. net/).
Bioinformatics annotation of proteins using ProtAnnDB ProtAnnDB is an annotation tool used for (i) selecting proteins to be included in WallProtDB and (ii) providing annotation of selected proteins. ProtAnnDB collects the results of bioinformatics predictions of sub-cellular localization and functional domains using available programs  [17]. The following programs or databases have been used for prediction of sub-cellular localization: SignalP (http://www. cbs. dtu. dk/services/SignalP/)  [21] , TargetP (http://www. cbs. dtu. dk/services/TargetP/)  [22] , Predotar (http://urgi. versailles. inra. fr/predotar/predotar. html) [23] , Aramemnon (http:// aramemnon. botanik. uni-koeln. de/) [24] , TMHMM (http:// www. cbs. dtu. dk/services/TMHMM-2. 0/), GPIsom (http:// gpi. unibe. ch/) [25]  and PredGPI (http://gpcr.
biocomp. unibo. it/predgpi/pred. htm) [26]. The databases used for the prediction of functional domains are Pfam  [18] , InterPro  [19]  and PROSITE (http://prosite. expasy. org/) [27]. Pro-tAnnDB also offers links to other databases providing genomic or gene regulation data such as Phytozome which collects genomic data (http://www. phytozome. net) and PlaNet which provides co-expression networks (http:// aranet. mpimp-golm. mpg. de/) [28]. PlaNet has been chosen because it gives information on all the A. thaliana genes as well as on other plant species. ProtAnnDB has also links to Aramemnon which presently contains membrane protein data for nine plant species (http://aramemnon. botanik. unikoeln. de/index. ep) and to two databases which collect expert annotation on cell wall protein families: (i) the Peroxi-Base which is dedicated to peroxidases toulouse.
Tools for browsing WallProtDB They are three ways to query WallProtDB: "Detailed search", "Summarized search" or "Blast search". (   6 ) keyword. These criteria can be combined to refine comparisons. The result of the query is a customizable table that can be exported in different formats such as a tab delimited text, an excel sheet or a pdf file (Figure  2 ). Alternatively, they can be directly printed. Hyperlinks lead to ProtAnnDB bioinformatics annotation, experimental flowcharts and MS data (Figure  2 ). Protein sequences can be retrieved in FASTA format. (2) The "Summarized search" interface provides tools for overall proteome comparisons. The result of the query is a table in which the numbers of proteins in each (i) protein functional class, (ii) protein family or (iii) protein (putative) function are indicated (Figure  3 ).
As mentioned above, different formats are available for export of query results. It is also possible to draw a Venn diagram to visualize proteome comparisons within a plant species (Figure  4 ). All the figures are clickable, thus enabling retrieval of lists of the corresponding proteins. (3)The "Blast search"  [32]  permits finding sequences homologous to a given nucleic or protein sequence in WallProtDB. A list of hits is proposed together with the possibility to visualize sequence comparisons and to collect the protein sequences in the FASTA format. It allows clustering newly identified CWPs with proteins present in the database. Then, it is easier to link the presence of some protein clusters to different physiological conditions and/or to cell wall types. Figure  4  An example of comparison between different proteomes within a plant species using the Summarized search.
Utility and discussion At present, WallProtDB contains 2170 proteins and expressed sequence tags (ESTs) identified in 36 cell wall proteomics studies performed on 11 different plant species (8 dicots and 3 monocots) (Table  1 , Additional file 1). It also offers tools for comparisons between proteomes. Wall-ProtDB is regularly updated with newly published experimental data which are manually curated to obtain a homogeneous annotation (prediction of sub-cellular localization and functional domains of proteins). Only proteins having a signal peptide to address proteins to the secretion pathway and no known intracellular retention signal are included in the database. Proteins predicted to be plasma membrane proteins have been introduced in the database such as cellulose synthase, callose synthase or receptor kinases.
They have been identified through peptides located in their extracellular domain. They are not true CWPs, but since they are involved in cell wall biogenesis or in signal transduction, they might be of interest for people working in the plant biology field. In addition, WallProtDB contains information about the protocols used to obtain cell wall protein extracts and about the strategies to identify proteins by MS and bioinformatics, as well as MS data when available. Furthermore, WallProtDB provides a list of references in the cell wall proteomics field which is regularly updated and comprises experimental articles as well as reviews. WallProtDB complements other databases such as SUBcellular location database for Arabidopsis proteins (SUBA3, http://suba. plantenergy. uwa. edu. au/) [33] , The Plant Proteome Database (PPDB, http://ppdb.
tc. cornell. edu/)  [34]  and the cellwallgenomics database (http:// cellwall. genomics. purdue. edu/intro/index. html). In each of these databases, the gathering of information is done in a different way. In SUBA3, only A. thaliana proteins are listed and all the proteins identified in published proteomes are included. This can be misleading because proteins known to be intracellular can be found in proteomes called "cell wall proteomes". For example, the At5g38410 A. thaliana small subunit of RUBISCO is mentioned as an extracellular or a plasma membrane protein although it is a well described chloroplastic protein. However, since all the proteins identified in cell wall proteomes are listed, SUBA3 is useful to get access to leaderless proteins identified in cell wall proteomes. PPDB is devoted to A.
thaliana, Oryza sativa and Zea mays. It contains experimental MS data on proteins identified in different organs or sub-cellular compartments including the cell wall. Finally, the cellwallgenomics database provides repertoires of genes involved in cell wall biogenesis in A. thaliana, O. sativa, S. bicolor and Z. mays including intracellular proteins such as glycosyl transferases involved in the biosynthesis of cell wall polysaccharides. It also gives information on some mutants and on techniques useful to study cell wall biology, but no cell wall proteomics data.
Acknowledgements The authors are thankful to  Université Paul Sabatier (Toulouse, France)  and  Centre National de la Recherche Scientifique (CNRS)  for funding their research. They are thankful to  Drs Cécile Albenne ,  Hervé Canut ,  Christophe Dunand ,  Valérie Pacquit , and late  Pr Rafael Pont-Lezica  for stimulating discussions. They also wish to thank  Pr Klaas J van Wijk  at  Cornell University  for linking WallProtDB to the Plant Protein Database (PPDB). The  GenoToul Bioinformatics platform  is acknowledged for providing the JQuery jvenn plug-in and facilities (http://www. genotoul. fr/index. php?id=12). Received: 10 December 2014 Accepted: 6 January 2015.
(a) Endosymbiont sequence types Comparison of two endosymbiont gene fragments (see Methods) revealed three distinct haplotypes among our isolates: 'haplotype 1' comprising isolates H76 and H101, 'haplotype 2' comprising AF6, H9, H28, H30, H323, H343 and H402, and 'haplotype 3' comprising H15 and H85. Using the concatenated gene fragments, there was a sequence divergence of 0. 99% between haplotypes 1 and 2, of 1. 32% between 1 and 3, and of 1. 16% between 2 and 3. The accD and murE partial sequences are deposited in GenBank (accession nos. KP071733-KP071738), and the alignments are provided in the electronic supplementary material, figure  S1.
This highlights our still limited understanding of the factors that maintain strain variation in endosymbionts  [19] , as well as the coexistence of protected and unprotected hosts. Improving this understanding will require a more comprehensive assessment of multiple traits affected by symbionts and measuring their joint effects on fitness in the field rather than the laboratory-a challenging but necessary endeavour. Data accessibility. Data presented in this paper are available at http://dx. doi. org/10. 5061/dryad. r5954.
Acknowledgements. We thank  A. Gouskov ,  S. Morlie  `re and  R. Rouchet  for their assistance with this work, and especially  P. Rodriguez  for maintaining the collection of insect lines. L. C. and C. V. planned the experiment and wrote the manuscript, L. C. carried out the insect experiments, L. R. performed the qPCR, and J. -C. S. performed the symbiont sequence typing and edited the manuscript.
4; Leica) and a charged couple device camera (DFC 360FX; Leica). Confocal and superposed confocal/DIC images were acquired on SPE or SP5 confocal microscopes with a 63× objective (HCX Plan Apochromat, NA 1. 4; Leica). Images were acquired with the LAS AF software at 5-s intervals, except for Video 5 (20-s intervals), and assembled for illustration using ImageJ and Photoshop.
Image analysis and quantifications AB size and anterior aster-furrow distance were measured using ImageJ software. The presence of the polar body was used to orient the embryos along the anteroposterior axis. Position of the furrow tip during ingression was tracked manually in ImageJ. When the two furrow tips on each side of the embryo had very close positions along the anteroposterior axis, only the first ingressing tip was tracked (Fig. 6, A-C ). Quantifications of  and myosin (Fig. 7 , A and B) intensities were performed using ImageJ. For ANI-2, z-stacks were acquired (500-nm z-step) and the signal intensities of the brightest nine z-sections were summed; a 5-pixel-wide line was then drawn along the cortex separating AB and P1 and integrated density of the signal along this line was measured (cortical signal).
Acknowledgments We would like to thank  A. Maddox  for providing ANI-2 and NMY-2 antibodies and  R. Chan  for anti-SPD-1 antibody. Some worm strains were provided by the  Caenorhabidtis Genetics Center , which is funded by the  National Institutes of Health Office of Research Infrastructure Programs  ( P40 OD010440 ). N. Loyer helped with furrow tip tracking and  E. Daniel  and  R. Jugele  contributed to the earlier steps of this project. We thank  M. Gotta , in whose laboratory this project was initiated, and the  Microscopy Rennes Imaging Center  for assistance with microscopy. We would also like to thank  R. Le Borgne ,  S. Le Bras ,  L. Chesneau ,  S. Dixon ,  A. Echard ,  G. Gillard , and  J. Pécreaux  for discussions and critical reading of the manuscript.
Many other bracovirus genes have unknown origins and display no similarities to genes in data banks except with other bracovirus sequences. For example, Cotesia congregata bracovirus (CcBV) encodes 26 bracovirus specific gene families (named BV1 to BV26)  [18]. We previously reported that some viral circles were found to be reintegrated in the genome of different geographic strains of the wasp Cotesia sesamiae  [13, 26]. The occurrence of circle integrations back into wasp genomes probably reflects a broad integration ability of circles since it was recently shown that integration into the DNA of parasitized lepidopteran host cells is a part of the bracovirus life cycle. Indeed it was shown that Microplitis demolitor bracovirus circle integration into lepidopteran Pseudoplusia includens DNA occurs by a specific mechanism involving a conserved viral site named Host Integration Motif  [27].