dataesr/scientific-paragraphs-categorization huggingface dataset:
Arnaout et al empirically estimated hypermutations in humans to be about 5% to 8%  [14]. We generated datasets with 2%, 4%, 6% and 9% random substitutions along each read. Those datasets can be accessed at http://www. vidjil. org/data. Note that those substitutions are added to the errors that may have been produced by the sequencers. The results for the mutated datasets (see Figure  4 ) show that on reads with 6% additional mutations, the center of the window predicted by Vidjil differed from that predicted by the other programs by less than 15 positions in about 99. 4% of the cases. Vidjil shows again a greater concordance with IgBlast than with IMGT/HighV-QUEST. For VJ recombinations, such as in TRγ , a positional inaccuracy of up to 14 bp is not a problem because we are using 40 bp w-windows.
Evaluation of Vidjil sensitivity Note that the detection threshold depends directly on the number of reads actually sequenced. A recent study, Figure  3  Comparison of the predictions of the center of the window made with IgBlast, IMGT/HighV-QUEST, and the heuristic of Vidjil, on the 100,000 first reads of a diagnosis sample (Diag, top) of a patient with ALL and on a dilution (Scale-10 -5 , lower table and graph). For each pair of programs, the number shows the distance between the predictions of the center of the window overlapping the CDR3. These values show that Vidjil successfully predicted the center of the windows. Note that the two other tools provided much more information, with alignments to the germline databases, and in the case of IMGT/HighV-QUEST, further analysis of the junction sequence.
Note that TRγ does not show great diversity (18 distinct V genes and six distinct J genes according to the IMGT germline databases) and this clone has no inserted N-diversity region. Follow-up points. The concentration of clone #01, measured with standard procedures (compared with the total number of cells), was 94% for Diag, 0. 5% for Fu-1, 0. 05% for Fu-2, and ≤ 0. 5% for Fu-4. The ratios of the rearranged TRγ sequences show a similar evolution, even if there is some bias, which could be corrected with a better calibration of the wet-lab protocol.
Discussion High-throughput sequencers will eventually raise the detection threshold, as already reported by several studies. They will also provide full insight into the whole population of lymphocytes, with multiclonal analyses of such populations. We believe that these analyses will bring a better understanding of lymphoid malignancies, and more generally, of immunology. However, they require specifically adapted mapping and clustering tools. We have proposed new algorithms designed for data from high-throughput sequencers. We have not focused on the analysis of individual reads, but have instead based the method on the ultrafast detection of windows containing the actual recombination junctions. As a consequence, the Vidjil program can process large datasets in a few minutes, outperforming other methods that are more adapted to the full analysis of individual sequences.
This was confirmed both for raw TRγ data and for mutated data, showing that the method can gather clones with a dissimilarity of up to 6%, arising from random mutations mimicking hypermutations. We tested Vidjil on TRγ which is less diverse than other loci. Hence if Vidjil had a lack of reliability, we would have been able to identify it. On the contrary we observed that the results are consistent both with conventional methods and with software focusing on a more in-depth analysis. As the Vidjil heuristic is fast and reliable, it could be used as a pre-processing for other programs. Indeed the purpose of Vidjil is not to provide detailed information on a given sequence. Several software are designed for that purpose: For example, one may launch IMGT/V-QUEST, IgBlast, or, for IgH clones, iHMMune-align for an indepth analysis of the clones identified by Vidjil.
Starting from Vidjil strict definition of clones, one could also use software such as ClonalRelate  [37]  to further gather these clones and to study their relationship. Note that all the ratios were computed by taking the number of segmented reads as a reference, which ideally corresponds to the number of rearranged T or B cells in the studied system. This differs from the proportion of the total cells used in current protocols, which also include other mononucleic cells, such as precursor cells. The inclusion of a standard of known concentration could be used to calibrate these different ratios.
Temperature interpolation A detailed review on the data, its quality and the interpolation method are provided  (Baillieux et al. 2013). A number of 112 wells with temperature measurements were available from the existing LIAG database  (Agemar et al. 2012). They are classified according to their quality in categories A to C indicating undisturbed data (category A), disturbed logs or from bottom hole temperature (BHT) measurements (category B) and BHT with insufficient further information (category C). The distribution and quality of the data used in this study is provided in Figure  2. It becomes evident that category A data are unevenly distributed and are mainly available around the Soultz EGS site as well as towards the southeast of the area of investigation. In the northeastern part, there are few category B and a large number of category C data.
For this reason, data in the northeastern corner have been completed by a number of measurements outside the original study area. It should be mentioned that in the original data, temperature values are indicated with an error of up to ±8°C to 10°C depending on the depth range  (Agemar et al. 2012  and references therein). For example, at a depth of 2,000 m, measurements from categories B and C tend to underestimate the formation temperature by around 4°C to 6°C in the given database. For interpolation, we have chosen 3D Delaunay triangulation since the variograms of multi-Gaussian kriging reveals only limited fit with the Gaussian function  (Agemar et al. 2012 ) and thus, maintaining the exact temperature value for known locations is in favour of this kind of interpolation. Following triangulation, we have applied natural neighbour interpolation on a grid with 250-m sampling size in the X (easting) and Y (northing) directions and 100 m in the Z (depth) direction.
Geophysical data processing Apart from the interpreted seismic lines incorporated into the 3D geological model, in this study, we have investigated existing magnetic and gravity data for physical properties of the seismically identified fault zones. Therefore, existing data were analysed with reference to the local structural setting. In the area of investigation, a number of 837 homogenously distributed, ground magnetic data  (Edel et al. 1982; Papillon 1995)  were reduced to pole. Gravity data were taken from  (Rotstein et al. 2006 ) database. To avoid physical inconsistency in the gravity data, redundant datasets with offsets were removed and a total of 1,011 measurements were selected. The dataset provides a complete Bouguer anomaly with a reference density of 2,670 kg m -3 and a terrain correction using a digital elevation model with a grid spacing of 75 m, to a maximum radius of 22 km.
In order to investigate the contribution of structures at different depth to the Bouguer anomaly, a pseudo-tomography has been obtained using a number of low-pass Butterworth filter wavelengths between 20 and 150 km  (Butterworth 1930) , on an extended area of investigation of 150 × 150 km using the entire graben gravity database outside  (Rotstein et al. 2006 ) and the selected data inside the area of investigation. The presented study area is centred in the extended area. A detailed sensitivity study on the potential of preferential filtering is presented  (Abdelfettah et al. 2014). Gravity forward modelling was carried out using Pohanka's algorithm  (Pohanka 1988 ) applied to a finite element model. The applied densities are obtained by comparing measurements in Soultz boreholes  (Genter 1990; Grecksch et al.
The intersection of CTA with the eastward deepening top basement reveals a more detailed pattern of three temperature anomalies (Figure  3 ). The quality of the interpolation is indicated by the number of measurements in the basement or with different distances to it. Both anomalies at Soultz and Rittershoffen reach CTA values of >85°C and are constrained by temperature logs into the basement  (Agemar et al. 2012 ). The latter reference from Villadangos (pers. comm. ) is not included in the database and thus, does not appear in Figure  3. The third anomaly close to Rohrlach is only little constrained by one measurement at approximately 500 m above the top basement. Comparing the location of the thermal anomalies to the structural setting obtained from the 3D geological model, we find that all three anomalies are located directly east of N-S-striking and W-dipping faults that in the case of Soultz and Rittershoffen represents Table  1  Densities used for the gravity forward modelling obtained by comparing measurements in Soultz boreholes  (Genter 1990; Grecksch et al.
Acknowledgements The authors would like to thank  LIAG-Hannover, BRGM  and  EEIG Soultz  and  EOST Strasbourg  for providing input data: borehole temperatures, seismic profiles and borehole geology for the creation of the 3D geological model, and geophysical data, respectively. J. -B. Edel (EOST) kindly provided the magnetic and gravity data; together with  A. Genter  ( EEIG Soultz ), we would like to thank them also for fruitful discussions. We would also like to thank  B. Valley  ( ETHZ-UNINE ) who provided necessary computing scripts, P. Renard (UNINE),  P. Altwegg  ( UNINE ) and  L. Guglielmetti  ( UNINE ) for their technical support and the three anonymous reviewers who helped improving the manuscript. This study was mainly conducted in the framework of the PhD of  Paul Baillieux  financed by  Canton of Neuchâtel (Switzerland).
The concepts within this level have been derived from the Highfleet Upper Level Ontology (ULO) (Highfleet). One aim of the FLEXINET project is to develop a reference ontology by applying a heavyweight ontological approach, this being the Knowledge Frame Language (KFL) which is based upon common logic (ISO/IEC 24707:2007). The approach is being realised by utilising the Highfleet Integrated Ontology Development Environment (IODE) which is enabling the ontologies to be queried and applied to the end user needs to develop solutions. This approach will ameliorate levels of semantic representation and definition, with a view to enabling a common base for seamless interoperability. The FLEXINET levels each inherit concepts from the respective level above but, also provide concepts to the level below; each of the levels becomes more specialised or domain specific.
We introduce here Momocs, a package intended to ease and popularize modern morphometrics with R, and particularly outline analysis, which aims to extract quantitative variables from shapes. It mostly hinges on the functions published in the book entitled Modern Morphometrics Using R by Claude (2008). From outline extraction from raw data to multivariate analysis, Momocs provides an integrated and convenient toolkit to students and researchers who are, or may become, interested in describing the shape and its variation. The methods implemented so far in Momocs are introduced through a simplistic case study that aims to test if two sets of bottles have different shapes.
In this paper, however, only two dimensional shape will be considered, e. g. , three-dimensional objects will be viewed from one side and considered as represented by their projections on a plane. How to quantify the shape of the heart that was described above, and how to compare a given number of hearts to show variations among themselves? This paper aims to provide a didactic introduction to the shape concept, and particularly a branch of morphometrics relying on outline analysis. Finally, a case study of outline analysis is presented; it uses Momocs, the R package presented here, whose name stands for modern morphometrics, and is intended to simplify such morphometrical analyses.
Momocs: Analysis of outline variation using R This paper introduces Momocs (http://CRAN. R-project. org/package=Momocs), a package to analyze outlines of shapes, using R (R Core Team 2013). The package originated from some core functions published by one of us  (Claude 2008 ) and reviewed by  Bowman (2009). These functions were turned into an integrated framework and a standalone R package. Below are provided step-by-step guidelines for performing modern morphometrics, from which Momocs derives its name, using R. The package's vignette A Graphical Introduction to Momocs and Outline Analysis Using R (Bonhomme 2012) also provides an extensive description of the functions of the package. While both outline analysis and R (to a much greater extent) have been used in increasing measure, so far no dedicated tool has been aviable at CRAN.
Preliminaries Momocs is S4-oriented  (Chambers 1998 ) which has many advantages in terms of usage and programming: it prevents typing errors, provides validity checking, allows inheritance and encapsulation, etc. (see  Genolini 2008). In practical terms, lists of coordinates and matrices of harmonic coefficients, are handled through 'Coo' and 'Coe' class objects respectively, to which methods can be applied. For those not familiar with S4 objects, data stored in objects can be retrieved and used as classical S3 objects in R: matrices, factors, etc. The Momocs documentation provides an extensive description of these classes and the methods that can be applied to them. The case study presented below will focus on the basic (and probably typical) use of the package. All the following examples are based on the bottles dataset from the package (see ?bot and Figure  5 ).
Principal component analysis Momocs takes profit of the ade4 package by  Dray and Dufour (2007). The pca method can be used on a 'Coe' class object and performs a PCA with centering but no rescaling by default. In other words, the small-amplitude coefficients will contribute less than the first coefficients. It returns a 'dudi' object to which all suitable ade4 functions can also be applied. An almost exhaustive wrapper that gathers graphical functions from ade4 such as the display of eigenvalues, confidence ellipses and "stars", individual labeling, neighboring graphs, etc. and that also adds dedicated features such as the display of the morphological space is provided by dudi. plot. See ?dudi. pca for an exhaustive description of this highly tunable function. Plotting the PCA (Figure  10 ) is straightforward: below, we first compute elliptical Fourier analysis with 20 harmonics, get out a 'dudi' object, and finally plot it.
R> botF <-eFourier(bot, nb. h = 20) R> botD <-pca(botF) Every harmonic is represented on the x-axis; then the corresponding coefficients are multiplied by values that illustrate their removal (0), the normal shapes they lead to (1) or their exaggerated effect in shape reconstruction (2 and above). When the first harmonic is removed, on which all other ellipses "roll on", the reconstructed shape is obviously very bad. Exaggerated coefficients help to understand their contribution to the final shape, e. g. , the second harmonic multiplied 5 or 10 times indicates that it contributes to describe the bottleneck while the third and fourth harmonic contribute to the constriction on the middle of the bottle. The bottom figure illustrates the variation of every coefficient along the whole dataset. Here, both the B n and C n coefficients are very small depicting the (bilateral) symmetry of the studied shapes.
Multivariate analysis of variance We can test for a difference between subsets of shapes using multivariate analysis of variance (MANOVA), with every harmonic coefficient being considered as an homologous quantitative variable measured for every shape of the dataset. This can be achieved with: R> manova. Coe(botF, "type") The number or retained harmonics was not specified. The outcome of this analysis shows that shapes of the whisky and beer bottles significantly differ.
Perspectives for Momocs The R package Momocs is generic enough to become a gateway in analysis of outline variation outside the developmental biology and evolutionary biology for which it has been developed. Complementary techniques can easily be included, by the package's developers upon request or by third parties who have developed new mathematical approaches. Momocs stands for modern morphometrics but, so far, only deals with outline analysis. For instance, we plan alongside package updates, a better digitalization step including outline acquisition with the help of Bezier curves, complementary approaches of outline sampling such as local oversampling and additional smoothing algorithms, the integration of 3D algorithms and more gateways between Momocs and others morphometric programs.
Moreover, morphometric data, extracted as lists of coordinates are scarcely available on the web, but may be very useful for meta-analysis, development, and as support for course material. Momocs contains such datasets kindly provided by its users. We believe in such open Figure  12 : Some examples of the visualization of the deformation grids obtained using thin plate splines in Momocs. From top to bottom: a simple deformation grid; isodeformation lines; and a vector field that all depict the bindings required to pass from the average shapes for beer and whisky. data philosophy, as encouraged by the R Core Team and the many package developers, and hope that Momocs will become a hub for such activity.
Figure9: Analysis of harmonic coefficients after an elliptic Fourier computation on the bot dataset. The top figure shows the effect of every harmonic on the shape reconstruction. Every harmonic is represented on the x-axis; then the corresponding coefficients are multiplied by values that illustrate their removal (0), the normal shapes they lead to (1) or their exaggerated effect in shape reconstruction (2 and above). When the first harmonic is removed, on which all other ellipses "roll on", the reconstructed shape is obviously very bad. Exaggerated coefficients help to understand their contribution to the final shape, e. g. , the second harmonic multiplied 5 or 10 times indicates that it contributes to describe the bottleneck while the third and fourth harmonic contribute to the constriction on the middle of the bottle.
Acknowledgments We thank the reviewers for all of their work and valuable comments made during the review process. We warmly thank  Allan Bailur  for his invaluable help with the presentation in English, along with Anna Seastrand on earlier versions of this manuscript. ANR-BDIV-07-008  supported VB and CG for this study;  ANR-11-CEPL-002  and  ANR-11-BCV-008  supported JC for this study.
DNA extraction, amplification and sequencing When possible, we analyzed five mealybug individuals from each of the samples collected. In total, we extracted DNA from 215 mealybugs, with the DNeasy Blood and Tissue Kit (QIAGEN, Valencia, CA). We ensured that voucher specimens were available for morphological identification, by not crushing the specimens before extraction. Instead, we used the non-destructive method described by Malausa et al. [23]. We aimed to amplify and sequence five DNA loci known to be informative for species identification and providing sufficient data for the subsequent design of species-specific PCR primers for the identification kit (see next section): two slightly overlapping parts of the cytochrome oxidase subunit I (COI) gene, 28S-D2, internal transcribed spacer 2 (ITS2), and the leuA-16S region of the DNA of the symbiont Tremblaya princeps.
01  [30]. Sequences were compared by direct alignment, and any specimen sequence with a different nucleotide present at one or more positions was considered to constitute a different haplotype. We also used Haplotype Detector software (http://www2. sophia. inra. fr/urih/sophia_mart_fr/genotyping_tools. php) to distinguish between and sort the various haplotypes automatically. The sequences analyzed were deposited in GenBank for future access and use (Table  2 ). For rough species delimitation, we used the online version of ABGD -Automatic Barcode Gap Discovery (http://wwwabi. snv. jussieu. fr/public/abgd/), a tool that detects gaps in the sequence barcodes and limits the differences between groups, which are smaller between specimens from the same species and larger for specimens from different species  [31].
We used a prior maximal distance P = 1. 67 and a Jukes-Cantor MinSlope distance = 1. 000000. We carried out BLAST searches of the GenBank database from NCBI (http://www. ncbi. nlm. gov/BLAST) to identify similarities between our sequence dataset and sequences already published in the GenBank online database. For 28S, COI and 16S, we used the MEGABLAST method (for highly similar sequences), whereas we used the BLASTn method for ITS2. We generated a neighbor-joining tree based on the number of nucleotide differences between the multilocus haplotypes, with Mega4  [32] , to provide a visual representation of the data (this tree was not generated to provide phylogenetic information) (Figure  1 ).
Design of species-specific primers for the molecular identification kit We used SP-Designer  [37]  software to design species-specific primers, using the list of haplotypes (for each locus studied, separately) as input data. Briefly, SP Designer designs PCR primers that (i) should hybridize to only a set of sequences targeted by the user (e. g. all the sequences displayed by the individuals of one target species), and (ii) allow the amplification of a DNA fragment of the desired size. We designed PCR primers hybridizing specifically to the sequences of five species. The first four species targeted were those found in this study to be the most common in Brazil (see results): D. brevipes, Ps. viburni, Pl. citri and Ph. solenopsis. The fifth species, Planococcus ficus (Signoret), constitutes a major threat to vineyards worldwide, and was therefore also selected so that the identification kit would rapidly detect its occurrence in cases of new invasions.
PCR products were separated on a QIAxcel advanced system (QIAGEN), and analyzed with QIAxcel ScreenGel Software (QIAGEN). 1 ). All specimens of ABGD group 1 were morphologically identified as the root mealybug Dysmicoccus brevipes (Cockerell). Two multilocus haplotypes (MLH 01 and MLH 02) were observed, with variation observed only for COI. We obtained BLAST hits with Genbank sequences corresponding to D. brevipes, with sequences similarities of between 99 and 100%.
DNA and morphological characterization The second group (MLH 03 and MLH 04) was morphologically identified as Dysmicoccus sylvarum Williams and Granara de Willink. Genetic variation was observed only for COI. As this was the first time that DNA from D. sylvarum had been sequenced, no BLAST hits for this species were obtained with GenBank. Groups 3, 4 and 5 correspond to three different species from the genus Ferrisia Fullaway. Group 3 was identified morphologically   2 ) according to the last taxonomic revision of the genus  [35]. Group 6 (MLH 10) were morphologically identified as Phenacoccus parvus Morrison. BLAST hits revealed similarities of between 97% and 100% with Ph. parvus sequences identified in previous studies (Table  2 ). Group 7 (MLH 11 and MLH 12) was identified morphologically as Phenacoccus solenopsis Tinsley.