dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1998 ) and because we could not exactly assess the number of cases under 3 year of age (age categories in the database were 01 year old and 15 years old, so that the number of 0-3 years old cases is between 200 and 1,590 out of the 9,076 cases included in the database), this could have caused a bias in our results, although probably minor. Including non-food sources (such as pets) might be particularly important in the case of children. Also, measuring exposure as a frequency of consumption rather than as quantities consumed would be more accurate. This could not be done here, though the frequency was available in the INCA dataset, because of the necessary update of the consumption data collected in 1999 based on availability of the market (tonnage) data. As the active monitoring of food-sources is costly and not always developed for the production level (farm)  (Batz et al.
, 2005; Kirk et al. , 2008; Sofos, 2008) , in some countries passively acquired surveillance data can be the only alternative available. Based on the current study, passive data appear to be a potential complement for active datasets, even moreso when considering the costs of data acquisition. A systematic active surveillance system of food sources is costly and thus can only encompass a limited number of sources, whereas the costs of a passive system are less important and independent of the number of sources monitored  (Havelaar et al. , 2007) , which are important features in the design of a public health tool. Including additional sources through a passive surveillance system could have several advantages. Such a system would encompass more potential sources and could thus avoid mis-attributions.
Conclusion We here have addressed the problem of the impact of data quality on the attribution of human cases to food-animal sources of Salmonella when using the bayesian microbial subtyping approach. Our results indicate that it is possible to obtain reasonably reliable attribution estimates based on passively acquired data for the sources contamination. In addition, such cost-effective data can complement actively-acquired and representative prevalence data. The data we used have limitations which could be addressed by an improvement in human and animal datasets through enhancement of the subtyping system and the inclusion of other potential contamination sources, especially imported products. The Bayesian tool could also be improved with respect to the nature, dimension and interactions of the type-and source-parameters, which are of primary importance in the approach.
Acknowledgements This study was supported by the Délégation Générale de l'Armement, Defence Ministry. The authors wish to thank all members of the MedVetNet WP28 Working Group for the useful discussions, the Office for foodborne zoonoses and food microbiology of the General directorate for food from the French Ministry of Agriculture, all members of the CEB team at the French Agency for Food, Environmental and Occupational Health and Safety, Maisons Alfort, and the Salmonella NRC at the Pasteur Institute for the subtyping analyses undertaken, the members of the HQPAP, EBEP and EBEAC units at the French Agency for.
Number of cases and prevalence in the sources of Salmonella Sero-and Subtypes in the different food-animal Species and amount of foodstuffs available for consumption inFrance in 2005France in   (2004France in  -2007for the sources contamination), active surveillance dataset. the number of human cases corresponds to observed cases for all serotypes except Enteritidis and Typhimurium for which deterministically allocated numbers of cases are shown. **the serotypes that are present in the sources for the passive dataset only are bolded (i. e. they were not observed with the active surveillance in the sources).
Number of cases and prevalence in the sources of Salmonella Sero-and Subtypes in the different food-animal Species and amount of foodstuffs available for consumption in France in 2005, active surveillance dataset. the number of human cases corresponds to observed cases for all serotypes except Enteritidis and Typhimurium for which deterministically allocated numbers of cases are shown. **the serotypes that are present in the sources for the passive dataset only are bolded (i. e. they were not observed with the active surveillance in the sources).
00% 0. 00% Passive dataset 0. 00% 0. 00% 0. 00% 0. 00% 0. 00% 0. 14% Essen Serotype** Aberdeen S 61:k:1,5,7 Number of observed human cases* 0. 00% Layers 0. 00% 0. 00% 0. 00% Broilers Turkeys 0. 13% 0. 00% 0. 00% 0. 00% 0. 00% 0. 00% Proportion in sources (%) 0. 00% 0. 00% Pigs Cattle Sheep 0. 00% 0. 00% 0. 00% 0. 00% 0. 00% 57. 69% 0. 00% Sea products 0. 00% 0. 00% 0. 00% 0. 00% Ducks 0. 00% 0. 00% 0. 14% Other poultry 0. 00% ST1 Others 3. 24% 22. 69% 15. 79% 1. 34% 4. 77% 7. 41% 4. 23% 33. 33% 22. 54% 16. 90% 25. 96% 2. 88% 7. 50% 27. 50% 11. 02% 0. 00% 1. 66% 5. 82% ST2 ST3 Total number / prevalence Food amount 0. 00% 0. 00% 617 82 301 1. 57% 0. 31% 1273 84 842 0. 00% 4. 90% 796 18 967 1. 41% 3. 29% 213 161 971 0. 00% 0. 00% 71 144 504 0. 00% 0. 00% 104 20 232 7. 50% 0. 00% 40 111 764 0.
Immunocytochemistry, fluorescence imaging and fluorescence profiling Procedures for fixation, resin-embedding and sectioning of tobacco seeds were performed as described previously  (Lee et al. , 2012). Immunocytochemistry was performed on 0. 5 lm thick sections before fluorescence imaging as described previously  (Lee et al. 2012). Quantitative analysis of fluorescence intensities across cell walls in response to antibody binding was performed using ImageJ (http://rsbweb. nih. gov/ij/) as follows: the line tool was used to generate transects at right angles to the identified region of adhered cell walls, associated fluorescence intensities were analysed using the plot profile feature, and the data were averaged for three transect lines per antibody from the same cellwall region and adjusted to remove background fluorescence before plotting.
ACKNOWLEDGEMENTS We acknowledge funding from the  UK Biotechnology & Biological Sciences Research Council  ( BB/G024898/1 ) and from the  European Union Seventh Framework Programme  ( FP7 2007-2013)  under grant agreement  263916  ( WallTraC, Marie Curie Initial Training Network ). Some chromatography experiments were performed at the  Wellcome Trust Centre for Biomolecular Interactions, University of Leeds, UK. © 2013 The Authors The Plant Journal © 2013  John Wiley & Sons Ltd , The Plant Journal, (2013), 75, 1018-1027.
After removing the RIDL and ROCK controls from the dataset, we tested whether the observed frequencies of transgenic and non-transgenic crosses differed from the expected (equal frequency, based on a null hypothesis of equal competitiveness of the two male genotypes) using the x 2 test. Mating competitiveness: 10 =/5 R. We assessed the mating competitiveness of RIDL vs. ROCK males when caged with five ROCK females, using a range of males of the two strains (ratio of strains ROCK/RIDL: 10/0, 8/2, 5/5, 2/8, 0/10), with eight replicates of each. We recorded the number of eggs, the number of larvae hatching off-TET, the mortality of the resulting pupae. All surviving pupae were transferred to tubes and sexed if they reached adulthood. We compared the number of eggs in transgenic to non-transgenic crosses using the Mann-Whitney test.
LA are or have been employees or students of  Oxitec Ltd  and/or the  University of Oxford. Oxitec  and the  University of Oxford  hold intellectual property relating to the subject matter of this paper. This work was supported by  Institut Pasteur (Paris)  and funded in part by a grant to the  Regents of the University of California  from the  Foundation for the National Institutes of Health  through the  Grand Challenges in Global Health initiative. This study was partially funded by  EU  grant  FP7-261504  EDENext and is catalogued by the EDENext Steering Committee as EDENext112 (http://www. edenext. eu). The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the  European Commission. All other.
Scanning Electron Microscopy and Energy Dispersive Xray Analyses Cells from late log phase cultures (OD 580 = 0. 7) were fixed overnight with glutaraldehyde (2. 5%) and alcian blue (0. 15%), washed twice with UPW and dried in CO 2 critical point dryer (BAL-TEC CPD030) as described  [20]. Samples were mounted on aluminium stubs using double-sided carbon tape, coated with carbon, and observed with a Zeiss Ultra 55 FEG SEM microscope, operated at 2. 0 kV or 10 kV, at a working distance of 2. 7 mm or 7. 5 mm. Images were acquired in secondary electron mode using a Everhart Thornley or the InLens detectors in backscaterred electron mode. Energy dispersive x-ray spectrometry analyses were performed with an EDS QUANTAX microanalyzer operated with the Esprit, Hypermap software allowing acquisition of X-ray maps and drift correction.
For all those reasons, they are often used as post-processing of genotyping basedphasing as for instance in Haplotype Improver  [18]. A recent work uses a different approach that simultaneously assembles reads and predicts haplotypes using colored De Bruijn graphs  [22] , a dedicated variant of the De Bruijn graphs  [23]. The associated CORTEX software  [24]  uses individual information to predict haplotypes. Disentangling homeologous copies can be seen as a quite similar problem except that, in allo-polyploid species, no easy solution exists to allocate a distinct colour to reads coming from the two homeologous copies with the exception of chromosome sorting  [25]. In this paper, we propose a solution, dedicated to allotetraploid species, that uses the nucleotide counts observed at each site, which is much more informative than using just accession genotypes but much more lightweight than using full read information.
This allows us to design a fast dedicated solution that identifies contigs for which a heterozygous excess may sign the assembly of two homeologous/paralogous copies. On these contigs, we propose a likelihood model-based method to rebuild mixed contigs in two new sequences based on their differential expression, a largely documented phenomenon on homeologous copies  [26] [27] [28]. We test the new sequences for their distances to the diploid progenitors of the allo-tetraploid species and verify their efficiency to map properly reads and to provide a significant increased amount of new valuable SNPs compared to de novo mapping method. The method is implemented in the HomeoSplitter software, tested and evaluated on durum wheat (Triticum turgidum L. ), an allo-tetraploid inbreeding species and is validated on the reference transcriptomes of its two diploid progenitors.
Accessions, cDNA extraction and preparation The diploid reference transcriptomes were assembled from the cDNA libraries obtained on a Triticum urartu accession (dv1792, kindly provided by Pr J. Dvorak, UC Davis) and an Ae. speltoides var. speltoides accession from Turkey (USDA PI 542268, http://www. ars-grin. gov/npgs/). These two lines will be further called urartu and speltoides accessions. The 30 durum accessions analysed were issued from single seed descents of plants sampled in a base broadening population of durum wheat (see additional file 1). The averaged observed heterozygosity of the 30 descents was 0. 022 on 30 microsatellite loci. Taking allelic frequencies into account, the fixation index, Fis  [33]  was estimated at 0. 95 on the whole sample (data not shown). These 30 accessions will be called durum wheat accessions.
Detecting questionable sites and contigs We questioned the polymorphic sites obtained from the mapping of reads on the de novo contigs (see Figure  2 ). First, a site s is here considered to be heterozygous for one accession a if at least two different nucleotides n 1 and n 2 are each observed at least 5 times (e. g. , sites pointed by red arrows in Figure  2A ). More formally, those sites are those such as: ∃n 1, , n 2 |1 ≤ n 1 < n 2 ≤ 4; Nb[a][s][n 1 ] ≥ 5; Nb[a][s][n 2 ] ≥ 5 We then define a site as questionable if it is found heterozygous in at least one eighth of the genotyped individuals out of the 30 possible durum accessions. These two parameters can be tuned by users according to their biological datasets. The 5X coverage threshold limit the impact of sequencing errors and the 1/8 threshold is adapted to our almost fixed accessions.
Hence the necessity to consider both term simultaneously. For instance if there is a single accession and a single questionable site where 49 A, 50 C and 1 T are observed, the first term will be maximal when considering that C k1  [1]  is a T, since with p 1 = 1/100 it is highly probable to observe 1 T among 100 draws, but since it is also highly unlikely to observe only 50 C among 100 draws with p 2 = 0. 99 this split will be discarded in favour to the much more likely explanation that C k1 [1] is an A and C k2  [1]  is a C, or vice versa. Note also that these two equivalent solutions A/C or C/A do not have exactly the same probability under our model, and this will always be the case as soon as there are more than two nucleotides observed at the considered site. Finally, we assume independence between sites so that the likelihood of C k1 , C k2 is just the product of site probabilities along sites and accessions: Lk(C k1 , C k2 ) = P(Nb|C k1 , C k2 ) = s,a p(Nb[a][s]|C k1 [s], C k2 [s]) Heuristic used to search for the maximum likelihood split Our HomeoSplitter software offers two different strategies to determine the preferred split.
Software availability HomeoSplitter is freely available at http://bioweb. supagro. inra. fr/homeoSplitter/ under the French CeCILL licence, which is compatible with the GNU GPL one. The software has been developed in Java and can thus be run on Windows, Linux and Mac Operating Systems. It takes as input an ALR file representing the frequency of each nucleotide in the initial mapping  [35]  and provides the new set of split contigs in fasta format as output. The SAM format is the de facto standard to represent mapping  [36] , the software SAM2ALR  [35]  allows to transform SAM formatted files into the ALR file that HomeoSplitter takes as input. All threshold values used to detect questionable sites and contigs are parameterized and HomeoSplitter also accepts a set of questionable sites as input.
Starting from an ALR file containing reads counts for M accessions over a set of contigs, HomeoSplitter has a time complexity of O(NM) and a space complexity of O(N l M); with N l the length of the longest contigs and N the sum of contig lengths. These low complexities allow to run HomeoSplitter on standard desktop computer; for instance our dataset of 3,709 contigs was processed in less than four minutes on a MacBook Pro laptop (OS X. 6, 4 Go RAM, 2. 3 GHz Intel Core i7 CPU).
Considered dataset and benchmark assembly The number of cleaned reads varies from 1. 3 to 10. 8 millions among the 30 durum accessions, with an average of 4. 01 millions. Speltoides assembly was built with 20. 25 millions cleaned reads and that of urartu with 26. 7 millions. Speltoides assembly counts 16,891 contigs (sized by length > 500 bp) with an average size of 1,193 bp when urartu assembly has 21,907 sized contigs with an average size of 1,351 bp. The assembly of the durum reads coming from the 30 accessions yielded 27,820 de novo sized contigs of 1,055 bp on average. After clustering (CAP3 90% of similarity), a total of 40,895 contigs (urartu + speltoides + durum) are present in 13,166 clusters. Durum contigs are present in 11,630 clusters (88%). The different clustering configurations are as follows: (durum + speltoides + urartu): 6,057 clusters; (durum+ speltoides without urartu): 1,899 clusters; (durum + urartu without speltoides): 2,667 and durum alone: 1,007 clusters.
As the divergence may vary a lot among genes (here the average similarity between urartu and speltoides is 97. 5% with a standard deviation of 1. 03%), a fine tuning of stringency is difficult to determine for a whole transcriptome (or genome). Questionable sites were easily detected in the selfing durum wheat. For outbred species, the questionable sites may be detected by the likelihood approach used in the read2snp package  [5]. It compares the likelihood of data for the existence of paralogs in diploid species with any kind of mating system. It may be further included in future version of HomeoSplitter. HomeoSplitter does not exploit the individual paired-end read information as in phasing methods  [18] [19] [20]. A pipeline to disentangle homeologous copies based on such diploid phasing methods has just been published  [37].
Acknowledgements This work was partially funded by the  French Agence Nationale de la Recherche Investissements d'avenir / Bioinformatique :  ANR-10-BINF-01-02 , Ancestrome. YH was granted by the flagship project  Agropolis Resource Center for Crop Conservation, Adaptation and Diversity (ARCAD)  funded by the  Agropolis Fondation. INRA  funded the data production through its  Actions Incitatives program  ( EPO  project). Olivier Guillaume  and  Aziz Moussa  also contributed, through short student projects, to this work. Sequencing was kindly performed on the HiSeq2000 sequencer at the Montpellier Genomix (http://www. mgx. cnrs. fr)sequencing facility. We are grateful to the three anonymous referees whose comments helped us to improve this paper. This publication is contribution 2013-118 of the  Institut des Sciences de l'Evolution de Montpellier  ( UMR 5554CNRS ).
Study setting The study was carried out in seven out of eleven provinces of DR Congo (Figure  1 ). In Kinshasa, health care facilities involved in the detection and study of the epidemic increase of typhoid fever-associated peritonitis of 2004 were selected  [16]. Health care facilities in other provinces were recruited based on existence of microbiological laboratories, professional contacts and the accessibility to reliable shipment facilities. South of the equator (provinces of Bas-Congo, Kinshasa, Bandundu, Kasai Oriental, Kasai Occidental), the wet season runs from November till May, north of the equator (provinces of Equateur and Orientale), the wet season runs from April till October. Rainfall data were retrieved from http://www. dr-congo. climatemps. com/, using the data from Kinshasa (4u239S 15u269E) and Bambesa (3u279N 25u439E) as representative for the rainfall respectively south and north of the Equator (Figure  2 ).
Pulsed field gel electrophoresis Respectively 34 isolates of Salmonella Typhimurium and 16 of Salmonella Enteritidis were selected for pulsed field gel electrophoresis (PFGE). These isolates were representative according to geographic origin and year of isolation. PFGE was performed at the Institute of Public Health (IPH, Brussels) according to the Pulsenet protocol for Salmonella  [28] , using XbaI as restriction enzyme (New England Biolabs, Leusden, The Netherlands). Cluster analysis was performed with Bionumerics 5. 1 (Applied Maths NV, Sint-Martens-Latem, Belgium), with as comparison settings the Dice similarity coefficient and UPMGA dendrogram type (optimization 0. 50%, position tolerance 1. 50%). The obtained PFGE profiles were compared to PFGE profiles of Salmonella Typhimurium or Salmonella Enteritidis originating from Belgium (n = 15 and n = 7 respectively) and Cambodia (n = 4 and n = 7 respectively), already stored in the IPH database.
Pulsed field gel electrophoresis Among the 34 isolates of Salmonella Typhimurium and 16 of Salmonella Enteritidis analyzed in PFGE, respectively 19 and 10 different PFGE profiles were observed (Figure  S1 ). For Salmonella Typhimurium, PFGE profile T4 was most prevalent occurring in 20. 6% (7/34) of isolates from DR Congo. This profile also occurred in 40% (6/15) of Salmonella Typhimurium isolates from patients in Belgium stored in the IPH database. Typhimurium profile T3 (2/34) was also observed in a Salmonella Typhimurium isolate from Belgium. For Salmonella Enteritidis, profile E5 was most common (25. 0%, 4/16). Profile E2, observed in 2 isolates from DR Congo, corresponded to one of the common Salmonella Enteritidis profiles also observed in Belgium. season. Although we cannot completely exclude that shipment delays of several days to weeks might have resulted in lower recovery rates, preliminary survival studies suggested that Gram negative bacteria remain viable for at least 8 weeks in inoculated and grown blood culture bottles in the environmental conditions encountered in DR Congo.
Quantification of vibrios Tissue samples diluted in sterilized seawater (1:100) were spread on marine broth medium, thiosulfate-citrate-bile salts-sucrose (TCBS) agar and CHROMagar in Petri dishes to quantify cultivable bacteria and, more specifically, vibrios. The bacteriological media used in this study were obtained from Difco Laboratories. These plates were incubated at 22. 5°C for 48 h before counting the number of CFU. Only vibrios data collected on CHROMagar are presented here because CHROMagar is somewhat more specific for vibrios than is TCBS (e. g. Di Pinto et al. 2011) , and a corre lation with TCBS was found under the conditions used here.
Statistical analyses Survival curves of oysters as a function of treatment (for Expt A: separate or in cohabitation), seawater temperature (2 levels for Expt A, 8 levels for Expt B) and status (previously exposed or unexposed to field conditions [naïve]) were compared using the Lifetest procedure in SAS 9. 2 (SAS Institute). When differences were detected, χ 2 comparison tests were applied to determine which treatment combinations differed significantly. A significant threshold of 0. 05 was adopted for all statistical tests. No statistical tests were performed on OsHV-1 data collected in Expts A 1 and A 2. Results concerning OsHV-1 detection were very clear, and no further statistical analysis was performed. For Expt B, 3-way split-split plot ANOVAs were conducted to determine differences in the quantity of herpesvirus OsHV-1 DNA and vibrios detected in oyster tissues, as a function of seawater temperature, status of oysters (previously exposed to field conditions or challenged) and time (day of sampling).
The unit of replication was the tank in which the temperature was applied (n = 2 for each temperature). The main plots were seawater temperature levels  (13. 4, 14. 4, 15. 4, 16. 2, 17. 5, 21. 9, 26. 9 and 29. 0°C), subplots were status levels (previously exposed and challenged) and sub-subplots were sampling time. Here we used a mixed linear model, which modeled not only the means of our data but also their variances and covariances. The need for covariance parameters arose because the experimental units on which the variables were measured were grouped into clusters and repeated measurements were taken on the same experimental unit. The 'repeated' option was applied to the term 'time' to take into account temporal dependence (SAS 9. 1. 3, SAS Institute). Where differences were detected, least-squared means multiple comparison tests were used to determine which means were significantly different.
The authors also express their gratitude to P. Le Souchu for his technical assistance, to H. McCombie for revising the English version of this manuscript and 4 anonymous reviewers for their constructive comments. This work was supported by Ifremer (project 'Surmortalité' 2010-11 coordinated by N. Cochennec-Laureau) and is part of the GIGASSAT project funded by ANR-AGRO-BIOSPHERE No. ANR-12-AGRO-0001-01.