dataesr/scientific-paragraphs-categorization huggingface dataset:
SSR, ISBP and EST marker analysis ISBP and EST screening of the 1BS MTP pools and DNA of deletion lines was performed using standard PCR and electrophoresis protocols (Additional file 10). For SSRs, PCR reactions with the M13 protocol  [86]  were carried out as described in Additional file 10 in a final volume of 15 μl with 200 μM of each deoxyribonucleotide, 500 nM of M13 primer dye-labeled at its 5′end (Applied Biosystems), 50 nM of the forward M13tailed primer, 500 nM of the reverse primer, 0. 2 U of Taq polymerase (DreamTaq, Fermentas) with 1× of its appropriate buffer and 25 ng of template DNA. PCR amplification was conducted with the touchdown procedure. Amplified products were visualized and analyzed by an ABI 3130xl Genetic Analyzer and the GeneMapper v4. 0 software (Applied Biosystems).
LTC program availability and data deposition The LTC program may be downloaded from the website of the Institute of Evolution, University of Haifa  [17]. The 1BS BAC library is available from the Centre National de Ressources Génomiques Végétales (CNRGV), Toulouse, France  [87]. The genome browser of the physical map of the wheat chromosome 1BS and marker data are available from the URGI website  [33]. The Nimble-Gen array design and all microarray data have been deposited with ArrayExpress  [80]  under accession numbers A-MEXP-2314 for the wheat NimbleGen 40 K UniGene design, and E-MTAB-1912 for the wheat NimbleGen 40 K UniGene hybridization experiment. The Roche 454 sequences of the 1BS sorted chromosome are accessible from the European Bioinformatics Institute short-read archive under the accession number ERP000446.
Acknowledgements The authors wish to thank the  International Wheat Genome Sequencing Consortium  for generating the wheat chromosome survey sequences and making them available to us for analysis. This work was supported by the  European Community 's  Seventh Framework Programme TriticeaeGenome  (grant agreement number  FP7-212019 ), the  Israel Science Foundation (ISF)  grants  205/08  and  800/10 , ISF equipment grants  1478/04  and  1719/08  and  Binational Agricultural Research and Development Fund (BARD)  grant  3873/06. Dina Raats  is grateful for the  Eshkol Fellowship  awarded by the  Israeli Ministry.
Genetic profile analysis Fragment size estimations and analysis of the similarities between genotypes were carried out using the BioNumerics® software (version 4. 1, Applied Maths, Kortrijk, Belgium). The similarities were calculated using Dice's coefficient with a maximum tolerance of 1% and dendrograms were built according to the Unweighted Pair Group Method (UPGMA)  (Struelens, 1996). The Simpson index  (Hunter and Gaston, 1988)  was calculated to estimate the diversity of the sample and the 95% confidence intervals were also calculated based on the variance as suggested by  Grundmann (2001).
Methods We searched in PubMed, papers published between January, 1980, and January, 2012 (inclusive), using the terms ((Disease outbreaks OR cross-infection OR Clusters) AND (listeriosis OR listeria monocytogenes)) AND (Food OR investigation). By reviewing the 288 retrieved records, we identified 42 reports on outbreaks or clusters of listeriosis, and among them 16 reports documenting food borne pointsource listeriosis outbreaks. Additional documented food-borne listeriosis cases or clusters were identified by reviewing the reference lists of the retrieved reports (n=3). Furthermore, we reviewed french listeriosis investigation reports of the Institut de Veille Sanitaire and identified data on incubation period in three reports of unpublished outbreaks and one report of a sporadic foodborne case.
Acknowledgements We are indebted to the clinicians, biologists and people from local health departments,  Institut Pasteur ,  Institut de Veille Sanitaire, Ministry of Health, Ministry of Agriculture and Ministry of Finance  who contribute to human listeriosis surveillance in France. We thank  Edith Laurent , for her efficient work and support. Received: 31 July 2012 Accepted: 18 December 2012 Published: 10 January 2013.
The purpose of this research first is to explore the texts and then to identify recurring themes. Moreover, we will seek to uncover styles of storytelling. Last, we try to measure a possible link between types of storytelling and prices of wines. Beyond the four goals we have previously defined, the aim of this article is twofold. First, through the corporate communications broadcasted by some of the most famous Bordeaux chateaux, we set up a typology that describes the various dimensions of the image of Grands Crus (fine or premium wines). Second, we investigate a possible link between communication styles and the level of future prices for the 2009 vintage. Section 2 is dedicated to a literature review. Section 3 presents the data and the methodology of our empirical analysis. Section 4 presents and discusses the results.
Storytelling Storytelling is defined as ''sharing of knowledge and experiences through narrative and anecdotes in order to communicate lessons, complex ideas, concepts, and causal connections''  (Sole and Gray Wilson, 1999p. 6 ). The firm is more and more considered to be a knowledge management system; the complexity of the narratives and their content constitute part of the nontangible aspect needed to understand current economic actors' behavior. Indeed,  Barthes (1985)  claims that the narrative is a tool to better understand the world. Concretely, narratives can be integrated into a database in order to analyze the knowledge they provide and to codify the stories a posteriori. The seeming spontaneity of a written text is one of the usual characteristics of storytelling, suggesting a high degree of diversity regarding the elements proposed in a story.
Then, each text was read a second time by the same researcher to assess the relative importance of every topic. The objective was to measure the intensity of the presence of the different themes on a specific scale, from 1 (no presence of the theme) to 4 (high intensity of the theme). This kind of analysis was suggested by  Gergaud and Chossat (2003)  in a case of comments provided by a gastronomic guidebook about French restaurants. These authors tried to explain the reputation of great chefs, measured by expert opinion, through objective data and texts written in guidebooks. The following text about Ch ˆateau Cheval Blanc is an example of what is provided in the annual guidebook published by the Union des Grands Crus association: ''In the 18th century, Cheval Blanc's current domain was mostly covered by an extensive vineyard, which can be seen in a Belleyme map from 1764.
Such an approach is useful because it forced us to engage in a twofold analysis. A lexicographic analysis conducted with a dedicated software (Sphinx Online-Lexica) allowing principal component analysis, correspondent factorial analysis, and analysis of variance (ANOVA) describes the corpus and structures it according to various classifications. Then, a multiple regression analysis highlights the link between the themes provided by the texts and the price level. The long-lasting tradition of price equations in wine economics provides a relevant framework for the left-hand side variables that potentially can explain price differences: weather conditions (Di  Vittorio and Ginsburgh, 1996) , sensory characteristics  (Combris et al. , 1997; Cardebat and Figuet, 2009) , technology  (Gergaud and Ginsburgh, 2010) , origin  (Nerlove, 1995; Schamel and Anderson, 2003) , individual and collective reputation  (Landon and Smith, 1997, 1998) , experts' ratings  (Dubois and Nauges, 2010) , frequently the grade given by the famous US critic Robert Parker (see  Lecocq and Visser, 2006 ; among others), quantity supplied  (Lima, 2006) , grape variety  (Steiner, 2004) , green labels and certifications  (Delmas and Grant, 2010) , and tasting notes  (Ramirez, 2010).
ACKNOWLEDGMENTS Cher-Pheng Ooi  is funded by a  DIM Ile de France postdoctoral fellowship  and by a  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant n •  ANR-10-LABX-62-IBEID ). The authors would likewise like to thank our laboratory members for their assistance.
We used mainly taxonomic tests and strains from winemaking environments of one geographical origin. This study was, to our best knowledge, the first attempt to computationally associate genotypic and phenotypic data of S. cerevisiae strains. We used subgroup discovery techniques to successfully identify strains with similar genetic characteristics (microsatellite alleles) that exhibited similar phenotypes. Within the present study we expanded the strain collection to 172 isolates from worldwide geographical origins and technological groups (wine, bread, sake, etc. ) and included 30 tests with biotechnological relevance for the selection of winemaking strains. Our objective was to gain a deeper understanding of the phenotypic diversity of a global strain collection and to infer computational models that predict the biotechnological potential or geographic origin of a strain from its phenotypic profile.
The first group of tests were performed in microplates using supplemented grape must, whereas a high reproducibility was obtained between experimental replicates. The second set of tests consisted in the evaluation of growth in solid culture media (BiGGY medium, Malt Extract Agar supplemented with ethanol and sodium metabisulfite). Galactosidase activity was evaluated by growth evaluation using Yeast Nitrogen Base supplemented with galactose, as indicated in the materials and methods section. After incubation, growth was evaluated by visual scoring (solid media) or by A 640 determination (liquid media). Table  1  summarizes the number of strains belonging to each of the phenotypic classes. Similarities between strains were evident, but each strain showed a unique phenotypic profile. A total of 5160 phenotypic data points were obtained, from 172 strains and 30 tests.
Prediction of technological group based on phenotypic results Our next objective was to construct a model that would predict strain's technological group from its phenotypic profile. K-nearest neighbour algorithm (kNN) and naı ¨ve Bayesian classifiers  [21] , as implemented in the Orange data mining software were used for modelling. The predictive performance of both classifiers was evaluated in terms of area under the Receiver-Operating-Characteristics (ROC) curve, using 5-fold cross validation  [22]. Table  4  shows the confusion matrix of naı ¨ve Bayesian classifications in test data sets of cross-validation; kNN results are not shown, as these were similar for both modelling techniques. Cross validated AUC score was 0. 70. Correct assignments were found for the larger groups of commercial wine strains and strains obtained from wine and vineyards, where 36 (77%) and 54 (73%) strains respectively, were accurately allocated.
Data analysis The phenotypic variability was evaluated by principal component analysis (PCA), available in the Unscrambler X software (Camo). The BioNumerics software (Applied Maths) was used for clustering, dendogram drawing and calculation of cophenetic correlation coefficients. Mann-Whitney test was applied to the phenotypic data set, including Bonferroni correction, to find relevant associations between phenotypic data and the strain's technological or geographical origin. A set of standard predictive data-mining methods, such as naı ¨ve Bayesian classifier and k nearest-neighbours algorithm  [21] , as implemented in the Orange data mining suite  [37, 38] , were used for the inference of prediction models. For prediction scoring, area under the receiver operating characteristics (ROC) curve (AUC) was used  [22] , which estimates the probability that the predictive model would correctly differentiate between distinct locations or distinct technological application or origins, given the associated pairs of strains.
Acknowledgments The authors would like to thank all the researchers that kindly provided yeast strains:  Gianni Liti ,  Institute of Genetics UK ,  Laura Carreto ,  CESAM  and  Biology Department Portugal ,  Goto-Yamamoto ,  NRIB Japan ,  Cletus Kurtzman ,  Microbial Properties Research USA ,  Rogelio Brandao ,  Laboratorio de Fisologia e Bioquı?mica de Microorganismos Brazil, Huseyin Erten, Cukurova University Turkey.
Ines Mendes and  Ricardo Franco-Duarte  are recipients of a fellowship from the  Portuguese Science Foundation ,  FCT  ( SFRH/BD/74798/2010 ,  SFRH/BD/ 48591/2008 , respectively) and  Joa ˜o Drumonde-Neves  is recipient of a fellowship from the  Azores government  ( M3. 1. 2/F/006/2008(DRCT) ). Financial support was obtained from  FEDER  funds through the program  COMPETE  and by national funds through FCT by the projects  FCOMP-01-0124-008775 (PTDC/AGR-ALI/103392/ 2008 ) and  PTDC/AGR-ALI/121062/2010. Lan Umek  and  Blaz Zupan  acknowledge financial support from  Slovene Research Agency  ( P2-0209 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Traditionally, the evolutionary scenarios of Asteropyginae have been organized in two major clades involving four pygidial patterns. The first cladistic analysis performed on the subfamily maintained the organization in two clades, though the phylogenetic relationships between genera were unexpected compared to the traditional scenarios. Moreover, the previous systematic scheme based on pygidial segmentation was rendered obsolete. In this study, we performed a new phylogenetic analysis from well-known taxa and recent discoveries. Sixty species assigned to 36 genera have been analysed from a dataset of 72 characters. Three more species are used as the outgroup. The 79 most parsimonious trees have a length of 492 steps and a retention index of 0. 735. Our analysis confirms the monophyly of Asteropyginae excluding the genus Protacanthina.
Analytical protocols We subjected our data matrix (online supplemental material, Appendix 2) to a parsimony analysis and used a heuristic search (tree bisection and reconnection, TBR) with 1000 random addition sequence replicates) using the software PAUP * 4. 0b10  (Swofford 2002)  to obtain the most parsimonious trees. Node robustness was assessed with a parsimony jackknife using fast addition sequence and TBR branch swapping, with 10,000 replicates and 36% character deletion  (Farris et al. 1996). As branch length is not used as subsequent information, synapomorphies are optimized under delayed transformation assumption (Deltran) in order to reduce ambiguous synapomorphies on internal nodes  (Agnarsson & Miller 2008).
Acknowledgements The authors thank  S. Regnault  ( Muséum d'Histoire Naturelle de Nantes, France ),  A. Folie  ( Royal Belgian Institute of Natural Sciences, Belgium ) and  S. Butts  ( Yale Peabody Museum of Natural History, USA ) who kindly offered access to their collections. This work benefited from the helpful improvement of the English and constructive suggestions made by  G. Edgecombe  ( Natural History Museum, UK ),  P. Morzadec  ( Rennes, France ),  B. Chatterton  ( Alberta, Canada ) and an anonymous reviewer. This paper is a contribution to UMR 8217  Géosystèmes du CNRS  and to  IGCP  596 ' Climate change and biodiversity patterns in the Mid-Palaeozoic (Early Devonian to Early Carboniferous) '. This study was also supported by the  Synthesys  Project (http://www. synthesys. info) which is funded by the  European Community Research Infrastructure Action  under the  FP6 'Structuring the European Research Area Programme'.
ANALYTICAL SOLUTIONS The present list of analytical solutions (TRAC v1. 4. 4) amounts to 14 in the advanced mode, but only 12 have been validated up to now. The other ones are in the validation process, which consists in comparing the results of a set of parameters with several other tools against TRAC. Other tools can be Excel R or Fortran R programs of the analytical solution, and mathematical modelling using the MARTHE finite-volume code  [17]. The list of symbols and notations used in the following formulas is given below (Table  2 ).
VALIDATION AND EXAMPLE TRAC has been compared to other ways of computation, either using a spreadsheet program such as EXCEL or a specific development of the analytical solution using FORTRAN language. The objective of these comparisons was to ensure a proper implementation of the formulas in the code. No differences at all were noticed. This validates the tool, but does not validate the formula, which is however supposed to be valid within the limits of its application field, as most of the formulas were already tested in the referenced publications from where they come. In order to validate the formulas as well, numerical simulations were conducted using a finite volume numerical model, MARTHE  [21]. The studied domain is discretised in meshes. Grid extension and boundary conditions depends on the type of problem.
Models The climate model used in this study, CLIMBER2. 4, is a coupled atmosphere-ocean-vegetation model. It is a revised version of CLIMBER2. 3 thoroughly described in  Petoukhov et al. (2000). The atmospheric and land surface modules have a coarse spatial resolution of ∼ 51 • in longitude and 10 • in latitude. This atmospheric model follows a statistical-dynamical approach: prognostic variables are bi-dimensional (latitude, longitude) and the vertical profiles of temperature and humidity are reconstructed using the assumption of a universal vertical structure of the atmosphere. This model is designed to only resolve large-scale processes, but the effects of synoptic weather systems on heat and moisture transports are parameterised as diffusive terms. In the standard version of the model  (Petoukhov et al.
, 2000) , stationary waves are parameterised as a function of the difference between sea-level temperature and its zonal mean. In the present study, we removed this ad hoc parameterisation which was at the origin of a cold bias over Beringia. The ocean is described by three latitude-depth (2. 5 • × 20 uneven vertical layers) basins (Atlantic, Indian, Pacific) including the Arctic and the Austral Oceans. The main difference between CLIMBER2. 4 and CLIMBER2. 3 relies on a different oceanic advection scheme, which is of second order type in the new version and less diffusive. The ocean module also includes a thermodynamic sea ice model which predicts the sea ice fraction and thickness for each grid cell with a simple treatment of diffusion and advection. The dynamical terrestrial vegetation model, VECODE, provides the distribution of trees, grasses and deserts in response to climate forcing.
Each of these three surface types may be or not covered by snow. The atmosphere and vegetation modules are linked through an atmosphere-surface interface scheme describing the land surface processes. This interface module considers six surface types: open ocean, sea ice, trees, grasses, deserts and glaciers, with the possibility for all of these surface types to coexist in one grid cell. The main advantage of CLIMBER compared to many other Earth models of intermediate complexity  (Claussen et al. , 2002)  is that it includes a full description the hydrological cycle: evaporation, humidity transport and precipitation are explicitly computed. Therefore, it is fully appropriate to be coupled with an ice-sheet model. Bonelli et al. (2009) ,  Calov et al. (2005a, b)  and  Ganopolski et al. (2010)  used the same kind of approach to account for the impact of dust deposition on snow and ice albedos.
Present-day simulations In the previous section, we have shown that the FST09 PDD formulation provides a more realistic geometry of LGM ice sheets with respect to available reconstructions  (Peltier, 2004; Lambeck et al. , 2006; Tarasov et al. , 2012). However, these results are likely to be model-dependent. To investigate the impact of potential CLIMBER biases on the current results we carried out GRISLI simulations (see Table  2a ) forced during 50 kyr with the present-day climate given either by CLIMBER (runs 4 to 6) or by the ERAinterim database  (Dee et al. , 2011 ) (runs 7 to 9). These runs have been performed with the three PDD models without accounting for ice-related feedbacks. The initial icesheet topography is given by  Bamber et al. (2001) : data of ice thickness and bedrock elevation are interpolated on a 5 km × 5 km grid.
This gives to an estimation of the Greenland ice volume 2. 93 × 10 15 m 3. When interpolated on the GRISLI grid, the same dataset leads to a reduced ice volume of 2. 88 × 10 15 m 3 , denoted V ref in the following. This latter ice volume lies within the range of 2. 83-2. 97 × 10 15 m 3 given by various sensitivity experiments to model parameters in  Ritz et al. (1997). Figure  2  represents the magnitude of the ice volumes and the ice-covered areas obtained at the end of the simulation, when the equilibrium is reached. This figure shows that the simulated ice volume is larger when GRISLI is forced by climatic variables coming from ERA-Interim. This is directly related to larger accumulation rates (w. r. t. the CLIMBER climate forcing) over the entire Greenland ice sheet, especially over the southeastern margin (not shown).
Acknowledgements. The authors are very grateful to  Roger Braithwaite  and  Lev Tarasov  for their constructive comments that allowed to improve the writing of the manuscript. Chris Stokes  is also thanked for his editorial advices. This work has benefited from the useful contribution of  Jean-Yves Peterschmitt  and  Pauline Beghin  in doing the figures. This study was supported by  CEA ,  CNRS  and  UVSQ. Edited by:  C. R. Stokes The publication of this article is financed by  CNRS-INSU.
g. ,  Schwander et al. , 1993). This results in an increase of the mean age of the gases and a broadening of the age NOAA and OGI at Barrow, Alaska (solid lines). 12-month running means calculated from the monthly data are shown as dashed lines. OGI measurements are on the OGI scale, NOAA measurements are on the WMO-2004 scale. OGI data are from  Khalil and Rasmussen (1984 , 1988 , 1994) ; NOAA data are from ftp://ftp. cmdl. noaa. gov/ ccg. distribution with depth in the firn (e. g. ,  Rommelaere et al. , 1997; Schwander et al. , 1993; Trudinger et al. , 1997). Toward the bottom of the firn layer, solid ice layers begin to form that almost completely impede vertical gas diffusion; this is known as the "lock-in zone" (e. g. ,  Battle et al. , 1996). Mean gas age increases rapidly with depth in the lock-in zone (e.
, 2007; Ferretti et al. , 2005; Haan et al. , 1996; Haan and Raynaud, 1998; MacFarling Meure, 2004; Wang et al. , 2010) , although because of the relatively short atmospheric CO lifetime, these records do not provide much information about the [CO] trend in the NH high latitudes. In this paper we present recent [CO] measurements from firn air at the NEEM ice core site in northern Greenland, as well as prior unpublished measurements from the Summit and NGRIP sites in central Greenland. Results from the NOAA global monitoring network indicate that mean annual [CO] is homogeneous within a few nmol mol -1 north of about 40 • N  (Novelli et al. , 1998b) ; NOAA station  [CO]  data further indicate that [CO] over the Greenland ice sheet is representative of mean annual  [CO]  in the Arctic (Table  S4 ).