dataesr/scientific-paragraphs-categorization huggingface dataset:
Off-specular neutron reflectivity. As the neutron reflectivity spectra were recorded using 2D detectors the off specular neutron reflectivity (OSS) is also accessible. 46 The OSS intensity corresponds to the intensity scattered at a condition were y f a y. In this case the total momentum transfer also comprises an in-plane component: q x ¼ 2p l cos y f À cos y ð Þ : (3) This allows one to determine SLD variations parallel to the interface but due to the geometry the momentum transfers are typically two orders of magnitude smaller than in specular reflectometry and therefore the spatial resolution is much lower probing micrometer sized structures. The width of the reflected beam was also extracted from the COSMOS data reduction software in order to monitor any broadening of the specular reflected beam coming from e.
Therefore the estimated resolution concerning the concentration profile roughness of BA can be estimated to be around 10 nm. In addition to the ''Gaussian'' interface roughness a linear gradient in SLD spanning over the whole LA layer could be fitted to the reflectivity curves without worsening the fit quality. This profile corresponds to a 10% D 2 O concentration difference between the two interfaces of the polymer layer. However, a completely flat SLD profile fits the data equally good for BA concentrations below 0. 3% and therefore a constant profile for both BA and D 2 O is used for the fits and the swollen thickness and the average concentrations are calculated. The second remark is whether the NR spectra can distinguish between a diffuse profile of water in the film or a (corrugated) rough film.
Furthermore, the increase in the reflected beam width (Fig. 6 ) suggests an enhancement of film buckling which points to an increase in the in-plane swelling along with the vertical swelling. The careful analysis of offspecular data shows no increased intensity in this regime pointing towards the absence of significant in-plane water inhomogeneities throughout the film. 3. 3. 3 Swelling at high BA solvent concentrations (between 0. 3% and 0. 5%). Above f BA/W = 0. 3%, the vertical swelling deduced from film thickness continues to increase with increasing f BA/W. However, the particularity of this regime is evidenced by the fast increase of SLD which could be interpreted as D 2 O invasion of the film (Fig. 4 ). Indeed, while the total swelling follows a similar trend as in the previous regime, the SLD rises abruptly, suggesting that the D 2 O is no longer excluded from the LA film for these compositions.
Acknowledgements We thank the  ILL  for neutron beam time on FIGARO (DOI:  10. 5291/ILL-DATA. 1-03-34 )and MLZ for neutron beam time on the N-Rex + instrument operated by  MPG at the Heinz Maier-Leibnitz Zentrum (MLZ), Garching, Germany. We thank the Partnership for Soft Condensed Matter at  ILL  and  ESRF  for the use of complimentary techniques. We thank  Alain Panzerella  from the  ESRF  for performing the AFM measurements. The authors gratefully acknowledge the financial support provided by  JCNS  to perform the neutron scattering measurements at the  Heinz Maier-Leibnitz Zentrum (MLZ), Garching, Germany. We acknowledge the help of Olaf Soltwedel during the NR measurements at MLZ. The Laboratoire Rheologie et Procedes is part of the  LabEx Tec 21 (Investissements d'Avenir - grant agreement no.
ANR-11-LABX-0030 ) and of the  PolyNat Carnot Institut  ( Investissements d'Avenir -grant  agreement n  ANR-11-CARN-030-01 ). We acknowledge  Dr Helene Galliard ,  Vincent Verdoot ,  Frederic Hugenell  for their technical support. We thank  Michel Manu  for fruitful discussions on the choice of the system to be used for this project. We acknowledge  BASF  for providing the varnish for this study. The open access fee was covered by  FILL2030 , a  European Union  project within the  European Commission 's  Horizon 2020 Research and Innovation programme  under grant agreement  N1731096.
Genetic linkage map construction For linkage mapping, a total of 173 simple sequence repeat (SSR) markers were tested with DNA samples from CS, V. riparia cv. Gloire de Montpellier (RGM), F1_148 and five individuals of the CS x RGM_F2 population. Among them, 47 new markers were developed for this study (Additional file 1). Four SSR markers had a monomorphic profile (VVMD25, UDV013, VMC3C11, VVBX07), five had a complex or multiloci profile (VVBX13, VVIH02, VVIM72, VVIV70, UDV061) and 18 were not reliably amplified, and were all removed from the analysis. The remaining 146 polymorphic markers were used to genotype the 337 individuals of the CS x RGM_F2 population and to construct and validate the genetic linkage map by the use of the softwares Carthagene and Join-Map® 3. 0 (Additional file 2).
Choice of molecular markers SSR markers were mainly chosen from the markers used to construct the map of the F1 CS X RGM1995-1 population  [9]. In addition, 46 new SSR markers were designed using the grape genome 12X sequence (http://www. genoscope. cns. fr/externe/GenomeBrowser/Vitis/, Additional file 1). Primers were designed with PRIMER0. 5 software (Whitehead Institute for Biomedical Research). A new insertion-deletion marker VvOMT2-2 was also designed based on the VviOMT2 gene sequence  [46]. A pair of primers, VvOMT2_ 2F:5′-AACTTTGCAGATGATAATCGAGG-3′ and VvOM T2_2R:5′-ATGGATTCGACATTGAGAAAATG-3′, were used to detect the presence an insertion-deletion of 7 bp in the 3'UTR of VviOMT2 gene (VIT_12s0059g01750).
Marker evaluation and genotyping PCR amplicons and GeneScan™ 600 LIZ® dye internal size standards (Life Technologies Corporation) were separated by capillary electrophoresis using ABI 3730 (Life Technologies Corporation). Markers were multiplexed by 12 or 13 per capillary channel by combining with Hamilton STARlet robot aliquots of 4-6 PCR reactions. Allele sizing was performed with ABI PRISM GENE-MAPPER 4. 0 software (Life Technologies Corporation) according to the manual instructions.
Linkage analysis and mapping The map was constructed using the software CarthaGene  [47]  at a logarithm of the odds (LOD) value of 5. 0 and at a maximal distance threshold of 35 cM. Validation of the map obtained was done using the software JoinMap® 3. 0  [48]  using a Kosambi's mapping function. The marker order obtained was checked according to the consensus map of the F1 population CS x RGM1995-1 and to the 12X genome sequence. The linkage groups (LGs) were numbered LG1-LG19, according to  [17].
QTL analysis Data normality for each quantitative trait was evaluated with Shapiro-Wilkinson test. Despite deviations from normality for each trait, data were not transformed because the interval mapping method is robust to deviations from this assumption  [50]. QTL detection was performed using the raw metric measurements with MapQTL 6. 0 software  [51]  and adapted from the literature  [9]. Four statistical methods were employed: Kruskal-Wallis analysis, interval mapping, MQM and permutation test. Four was retained as the maximum number of co-factors. The minimum LOD score used for QTLs detection was three. The significant LOD threshold was calculated at 5% for the LG and for the genome-wide through 1000 permutations. The maximum LOD value was retained for QTL position and a ± 2-LOD interval for the confidence interval.
Microarray analysis Roche Nimblegen oligonucleotide microarrays (Design 090918 Vitus exp. HX12) were used for whole genome transcriptome analysis. This microarray probe design for the 29,549 transcripts studied is based on the 12X genome assembly using the grapevine V1 gene model prediction from CRIBI (http://genomes. cribi. unipd. it/). The correspondence between probe identifiers and gene identifiers were obtained from CRIBI V2 (http://genomes. cribi. unipd. it). The microarray hybridisations were done by the Plateforme Biopuces, Institut National des Sciences Appliquées, Toulouse, France for the 10 individuals (five with a dwarfed and five with a normal phenotype); the protocol followed was as recommended by the manufacturer. R was used to analyze the microarray data  [49]  as described by  [52].
qPCR analysis For qPCR experiments, total RNA was treated with the Turbo DNA-free kit from Ambion to remove genomic DNA contamination and the reverse transcription was done using the Superscript III kit from Invitrogen (using oligo dT primers and 1. 5 μg RNA). Gene expression was analyzed with iQ Sybr Green Supermix on a Biorad CFX96 machine (primer concentration of 250 nM). The expression of genes of interest was normalised with SAND protein (VIT_206s0004g02820) and one additional reference gene were used to confirm the stability of expression of VIT_206s0004g02820 (Additional file 10). Two technical replicates were used in this study. PCR efficiency for each primer pair was calculated using LinRegPCR  [54]. Plasmid DNA preparation and insert size estimation DNA from CS bacterial artificial chromosome (BAC) clones VVCS1H006A20, VVCS1H011N07, VVCS1H012O11, VVC S1H12O17, VVCS1H018A11, VVCS1H065F12, VVC1H0 73F06 and VVCS1H03O10  [55]  was isolated using the Nucleobond Xtra Midi Plus kit (Macherey Nagel) according to the manufacturer's instructions with chloramphenicol selective marker (12.
PacBio assembly Assembly of the PacBio reads was performed following the HGAP workflow (https://github. com/PacificBiosciences/Bioinformatics-Training/wiki/HGAP [57]. The SMRT® Analysis (v2. 3. 0) software suite was used for HGAP implementation (https://github. com/PacificBiosciences/SMRT-Analysis). Reads were first aligned by BLASR (https://github. com/PacificBiosciences/blasr;  [58]  against "Escherichia coli strain K12 substrain DH10B complete genome". Identified E. coli reads and low quality reads (read quality < 0. 80 and read length < 500 bp) were removed from data. Filtered reads were then preassembled to generate long sequences. The sequences obtained were filtered against vectors sequences and the Celera assembler was used to get a draft assembly. The last step of HGAP workflow was the "polishing" that significantly reduced the remaining insertions/deletions and base substitution errors in the draft assembly.
In silico analyses of BAC insert sequences The obtained insert sequences of BAC clones VVC S1H018A11 and VVCS1H006A20 were aligned to 12X. v2 genome sequence  [59]  using MUMmer4 software  [60] ; https://github. com/mummer4/mummer). The presence of interspersed repeats and low complexity DNA sequences was evaluated using the RepeatMasker2. 1 software (https:// github. com/rmhubley/RepeatMasker).
Acknowledgements We thank  Christophe Boury ,  Agnès Destrac ,  Martine Donnart ,  Bernard Douens ,  Virginie Garcia ,  Nabil Girollet ,  Cyril Hévin ,  Ghislaine Hilbert ,  Elena Morin ,  Jean-Pierre Petit  and  Jean-Paul Robert  for their technical assistance. We thank the  Genome Transcriptome Facility of Bordeaux (PGTB)  for sequencing of microsatellites (Pierroton, France), the  Plateforme Biopuces  ( Institut National des Sciences Appliquées, Toulouse, France ) for microarray hybridisations, the  INRA-CNRGV  ( French Plant Genomic Resource Center, Castanet-Tolosan, France ) for the use of BAC sequences and the  GeT-PlaGe platform for BACs sequencing (Toulouse, France ).
Availability of data and materials The CS x RGM_F2 map is available with new SSR markers on https://doi. org/ 10. 15454/4ZUPCP. The raw and normalised microarray data is available on http://www. ebi. ac. uk/arrayexpress; the accession number is E-MTAB-2472. BAC insert sequences are available on https://doi. org/10. 15454/4ZUPCP.
Fluorescence emission spectra Fluorescence excitation and emission spectra were recorded with a HORIBA Jobin-Yvon FluoroMax-3 spectrofluorimeter, in asymmetric quartz cuvettes (path lengths of 1 × 0. 4 cm for emission and excitation beams, respectively). For each sample, two spectra were acquired using ex = 260 and 300 nm and emission range of 270-510 nm and 310-590 nm, respectively (slit widths: 5 nm for both excitation and emission beams). For both spectra, data pitch was fixed to 1 nm and integration time to 1 s. All spectra were corrected for the blank, recorded with the appropriate buffer, and truncated to the regions devoid of Raman or Rayleigh scattering bands (i. e. ex = 260 nm: em = 305-505 nm; ex = 300 nm: em = 350-585 nm). Of note, mathematical models allowing the suppression of scattering effects could also be applied for more precise corrections  (52, 53) ; however, due to the lack of significant emission and spectral variation in the regions where the scatters appear, we deemed this simple and straightforward process suitable for our scope.
Single-wavelength absorption measurements Absorption measurements were performed for all samples at 260, 300 and 400 nm, on a HITACHI U-2900 UV-VIS spectrophotometer. After zeroing of the absorption on a blank sample, the absorption of the sample at the three wavelengths was recorded in a quartz cuvette (1 cm path length). The data at 260 and 300 nm were then used to correct the emission spectra obtained upon excitation at these wavelengths. The absorption at 400 nm was used as an internal control.
Quantum yield measurements An appropriate aliquot of the DNA working solution was diluted to 1 ml with buffer A (except for EPBC and i-HRAS2 sequences which were tested in buffer B) so as to obtain a maximum absorbance value of 0. 11 at 265 nm. Afterwards, the sample fluorescence was recorded using ex = 265 nm and other parameters as described above. The resulting spectrum was integrated between 305 and 505 nm, after blank subtraction. 200 l of the solution were removed from the cuvette and substituted with 200 l of the appropriate buffer, and the absorption and emission were measured again. This procedure was performed four times, to obtain a total of five data points. The integrated areas were then plotted as a function of the absorbance at 265 nm and fitted to a linear model. The same protocol was applied to the reference (quinine sulfate in 0.
Principal component analysis of the emission spectral dataset To extract more information, the emission spectral dataset was subjected to principal component analysis (PCA). PCA is an unsupervised multivariate method, which describes the variance of the examined data matrix through a reduced number of variables and highlights the similarities between the analytes displaying similar response patterns. Thus, four emission spectra generated for each analyte (corresponding to two excitation wavelengths and two buffers, A and B) would account for 894 variables if the information obtained at each wavelength (1 nm data pitch) were to be used. to combine these data into a limited number of principal components (PCs) and facilitates their interpretation. In order to understand which spectra were more suitable for the analysis, we first performed a preliminary PCA on each separate set of spectra acquired with different excitation and buffer conditions.
In both buffers, the spectra obtained upon excitation at 300 nm were less suitable for the analysis (Supplementary Figure  S12 ). In fact, in both cases the separation between the groups was less efficient due to the little amount of variance described by PC2 (18. 4% and 16. 4% for buffers A and B, respectively). PCA of raw emission spectra (Figures  S8  and S9 ) was also attempted, but resulted in less efficient clustering (Supplementary Figure  S13 ), presumably due to strong intragroup variance of emission spectra G4 structures. In order to improve the clustering of iM structures and thus be able to detect the three separate groups (i. e. G4s, iMs and 'others', from now on used to designate the group of single-and double-strands) in a single plot, we attempted PCA on a combined dataset, including the spectra obtained upon 260-nm excitation in both buffers A and B.
We speculated that, since the data obtained in each buffer alone were not sufficient to reveal the cluster of iM structures, a change in fluorescence properties between the buffers A and B, specific for iM-forming sequences, could provide an additional information element. Indeed, the visualization and separation of the three groups was significantly better in this case, especially when using the first three principal components (Figure  7A ). The three principal components presented in the plot account for 82. 3% of the dataset variance. Inspection of the scree plot (Supplementary Figure  S14 ) shows that five components out of twenty that had been calculated are actually significant and describe up to 92. 3% of the dataset variance. In order to better understand the concrete meaning of each of the PCs, we examined the various 2D plots (PC1 versus PC2, PC1 versus PC3 and PC2 versus PC3, Figure  7B-D ).
Dataset reduction and linear discriminant analysis of emission spectra As a next step, we attempted to reduce the number of variables (and thus the dataset size) by selecting spectral regions that would be most relevant for sample grouping. Ideally, these should correspond to the wavelengths at which the analytes belonging to the same conformational group share similar spectral shapes, whereas the analytes from different groups show significant differences. To identify such zones, we calculated the intra-group variance for the normalized values obtained at each wavelength and, from the resulting three sets of values, the inter-group variance at each wavelength. These were plotted as a function of the wavelength (Supplementary Figure  S15 ). From this graph, four zones (324-333 nm and 374-393 nm for the spectra obtained in   buffer B; 333-343 nm and 378-388 nm for buffer A) in which the inter-group variance was relatively high were selected for further examination.
In this choice, we tried to favor the spectral zones in which the intra-group variance of iM structures was low, since this group showed the poorest clustering. The selected wavelengths account for 52 variables out of the 402 arising from the two combined spectra, corresponding to an 87. 5% reduction of the dataset size. PCA was ran on this reduced dataset, to verify the quality of the clustering under these conditions (Supplementary Figure  S16A ), and gave satisfactory results, with the three groups still appearing as relatively well separated. The resulting dataset was further reduced in order to subject to it to linear discriminant analysis (LDA). This is a supervised multivariate method, enabling to assign a test analyte to one of the a priori defined groups on the basis of the similarity of, in our case, emission spectrum profile, with those of the elements of the training set.
The limiting factor in LDA is constituted by the group sizes, since the number of variables cannot exceed the number of training samples in each group. In our case, the group of iMs only contains 14 oligonucleotides, thus restricting the number of exploitable variables to 14. We thus evenly selected 14 wavelengths out of the 52 identified ones and ran PCA again to ascertain that the information content and the groups separation were retained. Satisfyingly, this was the case (Supplementary Figure  S16B ), and we proceeded to perform LDA on the established dataset (Figure  8 ). Remarkably, DNA sequences were found to cluster relatively well, according to their conformation. Although the partial overlap of the clusters could not be avoided (in particular, those of iMs and 'other' sequences), the validation by the leave-one-out method confirmed a 90.
98 and 0. 80, respectively) and RND-HS1 and RND-HS2 to the group of single-and double-strands (Supplementary Table  S3 ); interestingly, the probability of RND-HS2 to belong to the G4 group was not negligible (P = 0. 095). To test the validity of the LDA prediction, we recorded 1 H NMR, CD and TDS spectra of the four test oligonucleotides. NMR data (Supplementary Figure  S18 ) showed that all four sequences showed the presence of imino proton peaks characteristic of the formation of secondary structures  (16). In the case of RND-HS1 sharp peaks were observed between 12. 5 and 14 ppm, indicative of formation of Watson-Crick base pairs and a duplex-type structure, despite the presence of a small hump at 10-11. 5 ppm. RND-HS2 displayed sharp peaks at 12. 8-13. 2 ppm indicative of a Watson-Crick base pair related structure and a broad peak centered at 10.
0 × 10 -4 to 2. 4 × 10 -3 ) and other sequences (Φ = 2. 8 × 10 -4 to 1. 4 × 10 -3 ). Interestingly, the quantum yields of left-handed G4 structures are almost as high as that of the G 3 T sequence (Φ = 3. 7 × 10 -3 in our conditions, i. e. almost 2-fold higher than 2 × 10 -3 as reported in (  46 )). The reason for this similarly enhanced fluorescence might reside in shared structural features. In fact, all reported left-handed G4 structures feature an interface of G4 blocks with a '5/6-ring' stacking mode of guanine residues  (62, 63). Notably, the same stacking mode is observed at the 5 -5 interface of the dimeric structure formed by a close analogue of G 3 T (J19, PDB: 2LE6, Supplementary Figure  S23 ), and was proposed to be at the origin of the exceptional fluorescence properties of this sequence, according to quantum chemical calculations  (42, 48).
However, a combination of the data obtained in two conditions (i. e. , favorable and unfavorable for iM folding) allowed a separation of iM-forming sequences as a separate cluster in a PCA plot (cf. Figure  7 ). Finally, supervised LDA of the reduced dataset (consisting of most informative wavelength readings) allowed a relatively good discrimination (error rate of 9. 1%) between the three distinct groups of G4, iM, and other (single-and double-stranded) oligonucleotide conformations (cf. Figure  8 ). Remarkably, the discrimination between the latter was not possible, neither by naked-eye inspection of the intensity or the shape of their fluorescence spectra (cf. Figures  4  and 5 ), nor by multivariate analysis of the latter. Notably, CD spectroscopy is not performing better in this case, since the CD spectra of these structural groups are relatively similar; in addition, single-stranded sequences demonstrate a significant intragroup variance in terms of CD spectra (Supplementary Figure  S7E, F ).
e61 11 PAGE 12 OF 16 Figure 9. 1112169 Figure 9. (A) PC1 versus PC2 versus PC3 plot obtained from the analysis of the training set (reduced dataset), supplemented with the emission data obtained for test sequences with peculiar conformations (G 3 T, ZG4, Block2Δ, 2xBlock2, VK1, VK2, VK34, (G 3 C 3 ) 3 , (G 3 C 3 ) 2 ), SC11, G3x, ss8, 24non096 and scr26). (B) LDA plot for the same dataset.
Introduction In a broad variety of domains, data are being gathered and stored at an intense pace due to the Internet and the widespread use of databases  [7]. This ongoing rapid growth of data that led to a new terminology 'big data' has created an immense need for a novel generation of computational techniques, theories and approaches to extract useful information, i. e. , knowledge, from these voluminous gathered data. These theories and approaches are the key elements of the emerging domain of knowledge discovery in databases (KDD)  [21]. More precisely, big data arise with many challenges such as in clustering  [35] , in classification  [34] , in mining  [38]  but mainly in dimensionality reduction and more precisely in feature selection as this is usually a source of potential data loss  [13].
6. 2). Actually, the used definition of these two metrics was based on the number of features per partition instead of the standard definition where the evaluation has to be based on the total number of features in the database used. In this paper, we propose a redesign of rough set theory for feature selection by giving a better definition of the work presented in  [8] , specifically when it comes to the validation of the method (Sect. 6). Our work, which is an extension of  [8] , is based on a distributed partitioning procedure, within a Spark/MapReduce paradigm, that makes our proposed solution scalable and effective in dealing with big data. For the validation of our method, and in contrast to  [8] , we believe that using the overall number of attributes is a much more natural setup as it will give insights into the performance depending on the input data set rather than the partitions.