dataesr/scientific-paragraphs-categorization huggingface dataset:
De Bruijn graphs and variations in the transcriptome A DBG is a directed graph G = (V , A) whose vertices V are labeled by words of length k over an alphabet. An arc in A links a vertex u to a vertex v if the suffix of length k -1 of u is equal to the prefix of v. The out and the in- degree of any vertex are therefore bounded by the size of the alphabet. In the case of NGS data, the k-mers cor- respond to all words of length k present in the reads of the input dataset, and only those. In relation to the classical DBG for all possible words of size k, the DBG for NGS data may then not be complete. Given two vertices s and t in G, an (s, t)-path is a path from s to t. As defined in  [7] , by a (s, t)-bubble, we mean two vertex-disjoint (s, t) -paths. This definition is, of course, not restricted to de Bruijn graphs.
Implementation and experimental results We now discuss the details necessary for an efficient implementation of Algorithm 1 and the results on two sets of experimental tests. For the first set, our goal is to compare the running time of Dijkstra's algorithm (for typical DBGs arising from applications) using several priority queue implementations. With the second set, our objective is to compare an implementation of Algorithm 1 to the Kissplice (version 1. 8. 1) algorithm  [6]. For both cases, we retrieved from the Short Read Archive (Accession code ERX141791) 14M Illumina 79 bp single-ended reads of a Drosophila melanogaster RNA-seq experiment. We then built the DBG for this dataset with k = 31 using the Minia algorithm  [9, 10]. In order to remove likely sequencing errors, we discarded all k-mers that are present less than three times in the dataset.
For this, we exploit another RNA-seq dataset, with deeper coverage. To this purpose, we retrieved 32M RNA-seq reads from human brain and 39M from human liver from the Short Read Archive (accession number ERP000546). Next we built the DBG with k = 31 for both datasets, then merged and decomposed the DBG into 128 BCCs (containing more than 4 vertices). We ran Algorithm 1 for each BCC with α 1 = 5,000. There were 114 bubbles with the length of the upper path strictly larger than 1,000 bp. In order to assess if.
Acknowledgements This work was funded by the  ANR-12-BS02-0008  (Colib'read); part of this work was supported by the  ABS4NGS   ANR  project ( ANR-11-BINF-0001-06 ) and Action n3. 6 Plan Cancer 2009-2013; and the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7 /2007-2013)/ERC Grant Agreement No. [247073]10.
Data handling and statistics Five soil layers were defined between 0 and 120 cm according to the soil horizon description of each soil pit (horizons were merged or divided in order to have five layers corresponding to the five sample depths; the profiles studied presented between four and seven horizons; the mean number of horizons of the 33 profiles studied is five). Assuming that elemental concentrations and soil densities measured in each of the five defined horizons were representative of the entire horizon, we computed the stock (in Mg ha -1 ) of each P pool using mean soil densities and horizon thicknesses: stock = 1 10 000 × [P] × d × h, (5) where  [P]  is the concentration of the P pool (in µg g -1 ), d the soil density (in g cm -3 ) and h the thickness of the soil horizon in cm. In litter, the P stocks were computed (in Mg ha -1 ) using the mass and the surface area sampled: stock = 1 10 000 × [P] × m litter , (6) where  [P]  is the concentration of the P pool (in µg g -1 ) and m litter the mass of litter (in g m -2 ).
Comparison on the global scale To compare the phosphorus status of our study sites with other grassland or forest ecosystems and with croplands, we compiled data on different P fractions in soils. In practice, we used different requests involving keywords such as "soil", "phosph*", "total content", "isotopic dilution", "isotopically exchangeable P", "grassland", "forest", "wood- CV: coefficient of variation (ratio of the standard deviation to the mean in %). land", etc. These requests were carried out both in Web of Science and Google Scholar. To derive the pools of diffusive and isotopically exchangeable phosphate ions, we selected all publications using the same isotopic dilution procedures as in the present study (i. e. Fardeau's procedure;  Fardeau, 1993 Fardeau, , 1996)). Additional publications were selected in order to improve the geographical coverage for total and organic P.
In particular, we examined all the tables of contents of the Soviet Soil Science and the Eurasian Soil Science journals to provide a better cover of northern Eurasian ecosystems. Based on all the selected references, we compiled a data set of different P fractions (total P, organic P, phosphate ions in soil solution, diffusive phosphate ions and isotopically exchangeable phosphate ions) in soils of grasslands, forests and croplands. This database contained P values for up to 373 distinct sites depending on the P fraction, the geographical scale and the vegetation type studied, which were collected from 236 references. This database was representative of soils throughout the world as shown by the geographical distribution of compiled sites (Fig. 1 ), even though the studies using the isotopic dilution kinetics method in forest were sparse.
Acknowledgements. We thank the anonymous reviewers who contributed to the improvement of this manuscript by their comments and suggestions during the whole review process for Biogeosciences. We are grateful to all the people who helped in the field in July 2013, notably  A. Bashuk ,  N. Gaberman ,  N. Kolosov ,  A. Litvinov ,  J. Petrashova ,  H. Rieckh  and  A. Stupak. N. Gallegos  ( INRA Bordeaux ) carried out part of the chemical analyses. B. Boitte  and  C. Tang  ( INRA Versailles ) as well as  M. -H. Bridet F. Brédoire  et al. : What is the P value of Siberian soils? and  M. Valentin  ( INRA Bordeaux ) provided support in the search for articles from the  journals Soviet Soil Science and Eurasian Soil Science. J. Regan  suggested the title. The  UR BEF  and UMR ISPA are supported by the  French National Research Agency  through the  Cluster of Excellence ARBRE  ( ANR-11-LABX-0002-01 ) and  COTE  ( ANR-10-LABX-45 ), respectively.
Collecting data in a web data base The first task was to gather all the actual knowledge related to the bridge and its surroundings within a database shared between the whole research team. The data gathering allowed to bring to light unpublished documents, both illustrated and textual, from medieval to contemporary times. In order to classify and share all the documentary sources according to relevant criteria, we built an on-line database based on PHP/MySQL (see figure  1 ) allowing researchers involved in the project to upload, document, comment and download documentary sources (texts, iconography, etc. ) according to their specific study's object. The continuous access to these documents was very helpful for the 3D virtual reconstruction issues, as they described -even sometimes in a very precise way -the former appearance of the area at several scales and temporal states.
Data gathering in a GIS datawarehouse All these data were geo-referenced and integrated into a H-GIS (Historical GIS). By integrating such heterogeneous data, in terms of space and time, this database permitted to enhance knowledge and analysis capacity of each document. Thus, the GIS became, besides a way to synthetize data, an important support for the scientific analysis. The gathered documents were processed according to their nature. Ancient maps were vectorised (land use, urban weft, foot print of remarkable buildings, etc. ). Ancient drawings were replaced in the estimated original position and orientation. This allowed to check, by comparing the elements represented on the drawing to the different maps and other drawings, the reliability of the artist.
Ancient Rhône and landscape restitution The former landscape restitution was made by stacking former maps and actual. By adding the on-field acquired data during the coring campaigns, two DEM were built as depth maps. These models were then transformed into meshes, and post processed with digital sculpting (ZBrush) software (see Figure  7 ), in order to get a more realistic appearance and UV coordinates. Subsampled models extracted from original ones have finally been generated and added to the GIS. The missing parts of this study were extrapolated from available data and geologist analysis, the accuracy and density decreasing as moving away from the bridge. This work has highlighted the modifications that affected the river during the last seven centuries: at the bridge origin, it was essentially made of meanderings, with low flow and small peeble islands -like Durance river today, close to Avignon.
In the motor condition, participants faced a 40 × 40 cm and 2 mm thin metallic surface on which two targets representing red flowers (3 cm of diameter) and distant 25 cm from each other were designed. In addition, a metallic thimble was fixed at the extremity of the index finger to make contact with one target or the other. Position of the targets induced an abduction movement of the arm from left to right (Fig. 1 ). The first target was used to detect the beginning of the arm reaching movement whereas the second target was used to detect the end of the arm movement. Timing data were recorded directly with the Audacity software allowing digital sound edition and recording. For the verbal condition, the sound was directly recorded via the tie microphone and software on an additional sound track.
Migration Cell migration was measured from 6 × 10 4 HUVECs with Bio-coat cell migration chambers (Becton-Dickinson, Le Pont de Claix, France)  [37]. Briefly, inserts of Bio-coat cell migration chamber were coated with fibronectin (100 µg/mL; Beckton Dickinson, Le Pont de-Claix, France). 6 × 10 4 HUVECs treated with SDC-1-, SDC-4-, EXT1-, EXT2-siRNA or SNC-siRNA for 48 h were resuspended in basal media supplemented or not with 10 µg/mL LMWF. Cells were added in the upper chamber and complete media was added in the lower chamber. After 24 h, cells migrated through the porous membrane were stained with Mayer's hemalum (Sigma-Aldrich, Saint-Quentin Fallavier, France) and counted manually by two different observers who performed the blind data acquisition. The cell migration rate was [(D1 -D2)/D1] × 100; D1 was the difference between the number of migrated SNC-siRNA-transfected cells stimulated by LMWF and that of unstimulated migrated SNC-siRNA-transfected cells; D2 was the difference between the number of migrated specific siRNA-transfected cells stimulated with LMWF and that of unstimulated specific siRNA-transfected cells.
In Vitro Angiogenesis Assay 2D-angiogenesis assay (capillary tube formation in Matrigel) was performed with 9 × 10 4 cells/well seeded on Matrigel-coated 24-well plate (Beckton Dickinson, Le Pont de-Claix, France) and treated for 24 h with 10 µg/mL LMWF. Endothelial cells were pre-treated with EXT1-, EXT2-siRNA or SNC-siRNA (control) 48 h before. The capillary tubes were fixed with 4% PFA and stained with 1% Hematoxylin (Sigma-Aldrich, Saint-Quentin Fallavier, France) and photographed in phase contrast microscopy (CK40; Olympus, Rungis, France) after 24 h. The average length of vascular capillary tubes was evaluated using the open source ImageJ Software (Open Source, ImageJ ver 1. 47r, National Institutes of Health, Bethesda, MD, USA).
For comparison α-actin (rabbit polyclonal IgG, I-19-R, dilution 1/500; Santa Cruz Biotechnology, Heidelberg, Germany) was used as relevant standard house-keeping protein and revealed with horseradish peroxidase (HRP) conjugated anti-rabbit IgG (dilution 1/2500; Jackson ImmunoResearch, Suffolk, UK). Proteins were detected using Enhanced chemiluminescence detection reagents (GE Healthcare, Orsay, France). The statistical analysis was done after the protein bands quantification by Scion Image Software (Scion Corporation, Frederick, MD, USA).
Acknowledgments This work was supported by the  Direction de la Recherche et des Enseignements Doctoraux (Ministère de l'Enseignement Supérieur et de la Recherche) , the  University Paris 13  and  INSERM. N. Marinval  and L. Maillard were supported by a fellowship from the  Ministère de l'Enseignement Supérieur et de la Recherche. The authors would like to thank  Liliane Louedec  ( INSERM U1148 ,  LVTS ) for her technical assistance with the animal experiments. This work was supported by  Region Ile-de-France doctoral fellowship for F. Chevalier  ( STEMP-RVT-UNIVPARISEST-AD-P10 ). The authors would like to thank  Julien Trébaux  and  Fabienne Chevallier  from  Cisbio Assays Company  for their technical assistance with the analysis of heparanase activity.
In a seminal paper, Meuwissen et al. [16]  proposed to use dense genotyping data from Single Nucleotide Polymorphisms (SNP) genotyping arrays as covariates in linear regression models for prediction of the genetic values for traits of interest. The method fits a predictive model in a "training population" of individuals for which both phenotypes and genotypes are known. Then, genomic estimated breeding values (GEBV) of test individuals are estimated using this model, based solely on genotypes. Individuals are finally selected based on their GEBV. The statistical apparatus of genomic selection is still in development and a whole range of methods have been devised  [17, 18]. As for genetic association studies, the question of the effect of genetic structure within the training population on the accuracy of the prediction is important.
2 μl LIZGS500 ladder and 9 μl HI-Di Formamide (Applied Biosystems). Fragments were separated by capillary electrophoresis on ABI3130xl (Applied Biosystems) and data were analysed using GeneMapper 3. 7 software. Additional file 2 lists the 29 SSR, 31 RBIP and 351 SNP markers used in this study and Figure  1  displays the positions of the markers placed on the pea genetic map according to Bordat et al. [25]. On this map, 274 SNP corresponded to 129 mapped loci. When their map positions were available, the markers used in the present study were placed on the pea genetic map according to  [25]. SSR (Simple Sequence Repeat) markers are in green and SNP (Single Nucleotide Polymorphism) markers are in red. replicated block trial on the 5 th March 2007, as described in  [26]. Eight to ten central plants were manually harvested in each row, in order to minimize border effects.
Statistical analysis For all markers, the number of alleles, the polymorphic index content (PIC) and the minor allele frequency (MAF) were computed. For all pairs of accessions and for each type of markers, pairwise distances (or Rogers' distances, RD  [27] ) were computed as the total number of polymorphic markers on the total number of markers scored. The mean number of polymorphic markers, the mean PIC and mean RD were calculated for each type of markers. The correlations among the three distance matrices obtained from the three types of markers were tested using Mantel's test available on the package ade4  [28]  of R software  [29]. The heatmap of pairwise similarities associated with the hierarchical complete linkage clustering of accessions based on the similarity (1 -RD) matrix (heatmap.
2 function in gplots R package  [30] ) revealed genetic relationships among accessions of the collection. The structure of the collection was also analysed using Discriminant Analysis of Principal Components (DAPC)  [23]  and INSTRUCT  [21]. DAPC relies on a transformation of the data by PCA thus ensuring that the variables of the transformed data are uncorrelated and their number is less than that of analysed individuals, followed by a discriminant analysis step. Since this method requires prior information about grouping of the individuals, a sequential k-means algorithm provided by the R package adegenet was run  [31]. The optimal number of clusters to describe the data is determined from the Bayesian Information Criterion (BIC) plot as a function of the number of clusters. INSTRUCT uses a Bayesian clustering algorithm similar to the algorithm implemented in the widely used STRUCTURE program  [20]  but allows for partial inbreeding.
Analyses were conducted allowing for admixture, for a number of groups K ranging from 1 to 7 with 5 MCMC chains per value of K, a burn-in period of 50,000 and a total of 100,000 iterations. The putative optimal K value was assessed by plotting the K value, following Evanno et al. [32]. ANOVA were performed on the discriminant axes of the DAPC analysis to detect the effect of passport information on the structure (proc GLM, SAS Institute). The phenotypic values of the 2007 field trial were analysed by a two-way ANOVA with a genotype and a block effect, and the mean values of phenotypic variables were calculated for each accession (proc GLM, SAS Institute). Broad-sense heritabilities were calculated as 1 -1/F, with F being the F value of the genotypic effect. Linkage disequilibrium r 2 values were calculated among 129 linked loci corresponding to 274 SNP using EggLib  [33]  and plotted against the genetic distance among markers, according to the genetic map in  [25].
Genetic prediction For the analyses of marker-phenotype association, 20 SNP markers were excluded from all the analyses due in particular to MAF ≤ 2%, too many missing data, or heterogeneous genotyping, resulting in 331 SNP which were used for analyses. Phenotypes were available for 367 accessions. Other missing data were imputed using Probabilistic Principal Component Analysis available in pcaMethods package  [34]  of Bioconductor  [35]. In the objective of predicting phenotypes from genotypic data, we compared the predictive performances of 6 statistical methods: LASSO (Least Absolute Shrinkage and Selection Operator)  [36] , PLS (Partial Least Squares)  [37] , SPLS (Sparse Partial Least Squares)  [38] , Bayes A, Bayes B  [16]  and GBLUP (Genomic Best Linear Unbiased Prediction)  [39]. These methods were tested on TSW, BegFlo and NSeed phenotypes measured in 2003 and 2007.
To avoid potential spurious associations caused by population structure, we used a new approach proposed by Johnson et al. [40]  and based on an Empirical Bayes (EB) algorithm to correct this undesirable effect. This method was designed to adjust for batch effects in microarray datasets. This analysis was performed using the Combat function of the SVA package of R software  [41]. Once this pre-processing step performed, the PLS, SPLS and LASSO methods were again used to predict the phenotypes.
The loadings w * h expresses the importance of each of the variables in the construction of the new components t h. The first components are sufficient to explain most of the data covariance. The SPLS regression  [38]  is a method derived from PLS which simultaneously offers a variable selection. Variables are selected by introducing a LASSO penalization on the loadings. The number of variables to be included in each latent variable is chosen empirically: 50 SNPs were thereby selected on each component to facilitate the biological interpretation. We used the mixOmics package  [43]  of R software to perform both PLS and SPLS analyses.
GBLUP (Genomic best linear unbiased prediction) VanRaden  [39]  suggested Genomic BLUP method where SNP markers are normally distributed with the same effects for all markers. The model considered is: y = μ + Zg + e ( 5 ) where y is the vector of phenotype, μ the overall mean, Z the incidence matrix of markers effects, g the vector of SNP effects where g ∼ N 0, Gσ 2 g. e is the vector of residual errors where e ∼ N 0, σ 2 e. G is the matrix of genomic relationship based on SNP and σ 2 g is the additive genetic variance. These analyses were performed using the rrBLUP R-package  [44].
The prior distribution of the variances is written as follows: σ 2 u = 0 w i t hp r o b a b i l i t yπ σ 2 u ∼ χ -2 (ν, S) with probability (1 -π) (8) For π, a probability π = 0. 95 was empirically chosen. Bayesian analyses were performed using the BGLR Rpackage  [45]  in which algorithms are based on a Gibbs Sampler.
Performance assessment of the methods The ability of each model (using the statistical methods) to correctly predict the phenotype of test genotypes was evaluated using different parameters. To that end, the dataset was split into a training and a test set composed respectively by 2/3 and 1/3 of the data. This ratio is commonly chosen in such situation. The training set is large enough to provide reliable estimation of the model and the test set is large enough to give an accurate assessment of the predictive ability of the model. This process was repeated 500 times; each time, the test and training sets were sampled at random (without replacement). For each iteration, models were created on the training set, applied to the test set and evaluated by computing mean squared error of prediction (MSEP) with: MSEP = 1 n test n test j=1 y j -ŷj 2 , ( 9 ) where n test is the total number of samples in test set, y j is the observed phenotype value for the j th test object and ŷj the value predicted on test set by the model creating with training set.
Genetic structure of the pea germplasm Our pea collection is a subset of the INRA collection and includes wild germplasm from the core16 of the JIC collection as well as landraces and cultivars from China, Russia, USA and Spain. This collection of 372 accessions was gathered in order to represent a wide range of phenotypic and passport data diversity. We confirmed (i) that the pea germplasm is structured according to major passport classes as defined by the use type, the population type, and the sowing type and (ii) that the range of genetic variability present in the Pisum sativum germplasm is high, as already pointed out by other authors  [4, 5, 8, 9, 53]. However, this passport information may be misleading in some cases: the fodder and winter-sowing types are ancestral characteristics present in wild germplasm but can correspond in the collection to contrasted levels of winter tolerance and to different growth habits.
Structure description is not unequivocal and group ascertainment is potentially prone to misassignments, probably because of (i) the difficulty to summarize all the information contained in large datasets (many variables and/or accessions) and (ii) marker information that may not be complete enough. Most studies conducted so far were based on rather limited number of markers probably introducing sampling biases. Baranger et al. [5]  mentioned that increasing the number of markers by pooling different types of markers was efficient in getting a clearer picture of genetic relationships. The markers used in the present study (Figure  1 ) represent the largest set used so far to characterize pea germplasm. Different methods describe genetic relationships among germplasm accessions. The methods proposed by STRUCTURE and INSTRUCT are extremely popular but rely on an explicit population genetics model which might make them sensitive to large departures from the postulated model.
Furthermore, when the dataset is large, the computational burden incurred can be substantial. DAPC, which makes no assumption on population genetics parameters has been increasingly applied to diversity studies and performed well especially in the case of large datasets  [23, 55]. In the present paper, the distance-based, INSTRUCT, and DAPC methods proved complementary. The congruence of hierarchical clustering based on SNP distances with INSTRUCT and DAPC is well visualized on Figure  3.