dataesr/scientific-paragraphs-categorization huggingface dataset:
Aerosol optical properties AERONET level 2. 0 retrievals for single scattering albedo (SSA) and complex refractive index are limited to measurements of AOD (440 nm) > 0. 4. The reason is that the accuracy of these two parameters significantly decreases under lower aerosol loading conditions: 80-100 % and 0. 05-0. 07 for real and imaginary parts of refractive index, respectively, and 0. 05 for SSA  (Dubovik et al. , 2000 (Dubovik et al. , , 2002)). Therefore, no information of these parameters is available in the AERONET database for the dry season at Tamanrasset. Regarding the wet season, dust optical properties (from March to October) are reported by  Kim et al. (2011). To perform an analysis for the dry season, we have filtered level 1. 5 data following Significant height levels, except the ground level, for the CWT analysis (2600 and 5600 m a.
The AERONET sun photometer at Tamanrasset has been calibrated within AERONET-EUROPE TNA supported by the PHOTONS and RIMA networks and partially financed by the European Community -Research Infrastructure Action under the Seventh Framework Programme (FP7/2007-2013) "Capacities" specific programme for Integrating Activities, ACTRIS grant agreement no. 262254. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and READY website (http://ready. arl. noaa. gov) used in this publication. We also thank Google Earth ™. Financial support from the Spanish MINECO (projects CGL2011-23413, CGL2012-33576 and CGL2012-37505) is also gratefully acknowledged. We are particularly grateful to the Tamanrasset Global Atmospheric Watch (GAW) station's staff (l'Office National de la Météorologie, Algeria) for supporting the measurement program.
melanogaster, and Drosophila. They diverged approximately 47 Mya ago, according to TimeTree2  [30]. Each subgenus is composed of multiple groups and subgroups, but Sophophora is most represented among the 20 sequenced genomes, with 17 species versus 3 for the Drosophila subgenus. An exhaustive homology-based search in the 20 genomes recovered 36 lineages of MLEs, most of them not described previously. Phylogenetic analysis suggested that the 36 lineages belong to 5 different subfamilies, including one not identified previously. Although the genome dataset is highly biased toward species from the melanogaster group (Sophophora subgenus), comparison of these different mariner lineages within the different species provides a view of this TE family within one genus and offers clues to the evolution of MLEs by revealing mariner elements at each stage of their life cycle.
The 10 remaining lineages grouped together into a highly supported clade that appears to correspond to a new subfamily, which we designated drosophila. The clade structure derived from the transposase analysis was supported by the comparison of TIRs, which showed that some blocks of nucleotides are clade (subfamily)-specific (see Additional file 3). For each lineage, we analyzed flanking regions to filter out duplicated copies (segmental duplication) and retain only independent copies resulting from transposition. All truncated copies located at contig ends were also discarded. This resulted in a clean dataset of 3085 copies, corresponding to 132 different MLE/species lineage combinations that were used in all further analyses (Figure  1B ). The copy number varies from 1 to 469 (Dromar6 in D.
eugracilis and one each for Dromar28 and Dromar36. All resulted from the replacement of the 3′ portion by the 5′ sequence (Figure  3B, C ). These rearrangements cause these copies to have unusually long TIRs. Hence, internal rearrangements appear to be a major process involved in the generation of MITEs. Figure  2  Experimental design procedure showing all steps of the analysis. We first searched by TBLASTN all 20 Drosophila using a set of 18 MLEs transposases representative of all described subfamilies of the mariner family. All sequences longer than 400 bp were clustered with a threshold of 80% identity. A consensus of each cluster was blasted against a transposase database composed of Tc1 family elements to exclude sequences from this family. For the remaining 36 clusters (bona fide MLEs), consensus conceptual translations were used in a phylogenetic analysis together with the MLEs transposases, and the nucleotide consensus were used as queries in a MEGABLAST search in the same 20 genomes to correctly identify all copies.
All hits plus 250 bp of flanking regions were retrieved. Structural and evolutionary analyses were performed on a clean dataset from which duplicated copies (segmental duplications) and incomplete copies (ends of contigs) were excluded. MITEs have been associated with genes in plant species  [32, 33]. We investigated whether this association was present in the high-copy-number Dromar11 and Dromar5 MITEs in D. eugracilis. Because this genome is not yet annotated, we first identified potentially coding regions using a TBLASTN search with the protein database of D. melanogaster and then computed the distance of Dromar11eug and Dromar5eug copies to these potential CDSs (Additional file 5). For both lineages, the proportion of copies present in the same supercontig as a putative CDS was higher for MITEs than for non-MITE copies.
Detection of mariner sequences The strategy used to recover MLEs is depicted in Figure  2. We used several programs from the BLAST + suite  [64]. Based on their availability in the protein database and their diversity, we selected 18 transposases (Table  1 ), covering at least 8 subfamilies, and used them as queries in a batch TBLASTN search performed on the WGS sequences of 20 Drosophila genomes  [28]  and Piano, Cherbas  [29] , using the default cut-off value. We recovered 14977 different hits (High Scoring Pairs) and reassembled them into 3694 copies according to the following criteria. Two hits from the same scaffold are grouped together if the distance between the center of each hit is less than 1000 bp and if they have the same orientation. Only copies longer than 400 pb were retained.
Analysis of amplification dynamics Species-specific amplification dynamics of single lineages were inferred using a new method based on the phylogenetic tree node distributions over time. This method relies on the topology of the tree and offers a visualization of the variation in transposition rate over time. More details are available in Le Rouzic, Payen, Hua-Van  [34]. The trees were rooted with an outgroup corresponding to the consensus sequence of the closest MLE lineage. All trees were reconstructed using FastTree 2. 1  [68]. All insertions were removed from the sequence dataset.
Acknowledgments This work was supported by  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) , the  Coordenção de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)  and the  Centre National de la Recherche Scientifique (CNRS). The first author of this manuscript was a " Bolsista da CAPES -Proc. N°6145116 ". We thank  Arnaud Le Rouzic  for his critique of the manuscript and valuable comments. We thank AJE for English editing.
Molecular dynamics A crystal structure is not available for each of the 16 studied systems. For some of them, the X-ray data are available but unfortunately they leave some protein structures incomplete. That is the reason why we selected only two CD1d structures: 2PO6 (human CD1d) and 3SDA (mouse CD1d) with full CD1d structures. Then, starting from the geometry of the KRN7000 ligand from PDB file 2PO6 we generated the OCH9, AZOL, SaGAL, and 7DW analogues. For this, the program molden  [34]  was used to achieve chemical alterations. Truncating the sphingosine chain to obtain the OCH9 compound was straightforward. Using the Z-matrix editor of Molden allowed us substituting the amide function by a 1,2,3-triazole group (AZOL compound), substituting an oxygen atom by a sulfur atom (SaGAL) and adding a phenyl group at the end of the shortened acyl chain (analogue 7DW).
In the primary sequence of CD1d, the mouse protein contains an insertion of two residues between residues 89 and 90 of human CD1d. This does not concern the binding domain. The preparation of the protein (disulfide bond linkages, protonation state of ionisable side chains) was achieved using the same protocol as in our previous study. [17]  In particular, as specified in the X-Ray structures, three disulfide covalent bonds were set between cysteine residues. We employed the package program xLeap of the AMBER11 package  [35]  to add the hydrogen atoms to the protein structure. The Propka  [36]  application was used to examine the protonation state of ionizable side chains with a focus on residues asparte, cysteine, histidine and glutamate in proximity to the ligand. No protonation state change was required compared to the state proposed by xLeap.
Counterions (Na + ) were added such that to neutralize the unit system. The ff99SB force field was used for the protein. The general amber force field GAFF was used in conjunction with the antechamber program to describe the ligands. [37]  The respgen procedure was employed to derive atomic charges from HF/6-31G* electrostatic potential calculations obtained with the Gaussian package  [38]. The ligand and protein CD1d were fully explicitly solvated in a truncated octahedral box using TIP3P water molecules with a buffer distance of 7. 0 A ˚and under periodic boundary conditions. We have carefully checked that the initial water molecules buffer remains during our simulations, and the CD1d protein does not interact with its neighbors in periodic images. The entire system consisted of about 40700 atoms (depending on the ligand).
Acknowledgments The computations reported in this work were performed using resources from the  Centre de Calcul de Champagne-Ardenne (ROMEO, University of Reims Champagne-Ardenne, France , http://romeo. univ-reims. fr)and the  Centre de Ressources Informatiques de Haute Normandie  ( CRIHAN, Rouen, France ). Also, we would like to thank the  Multi-scale Molecular Modeling Platform  ( P3M ,  University of Reims Champagne-Ardenne, France , http://p3m. univ-reims. fr)for various supports. We also thank the professors  M. Dauchez  and  A. Haudrechy  for helpful discussions.
Median gene and protein lengths are 1,718 bp and 407 aa, respectively, gene density is 364 genes Mb Ϫ1 , and the GϩC content is 56%. Less than 1% of the genome is repetitive, with virtually no detectable polymorphism. Analyses of the carbohydrate-active enzymes detected 85 enzymes from 34 glycoside hydrolase (GH) families, 47 enzymes from 22 glycosyltransferase (GT) families, and 11 en-zymes from two carbohydrate esterase (CE) families. The genome sequence of T. anomala provides the first genomic data for a member of the Exobasidiomycetes order Georgefisheriales as well as for a homothallic species of Ustilaginomycotina. Nucleotide sequence accession number. The genome sequences and annotations are available via the JGI fungal genomics resource MycoCosm  (10)  at http://genome. jgi. doe. gov/Tilletiaria/ and have been deposited at DDBJ/EMBL/GenBank under the accession no.
Instrumental parameters Analyses were performed on an Accela liquid chromatographic system (Thermo Fisher Scientific) coupled to an LTQ-Orbitrap Discovery (Thermo Fisher Scientific) fitted with an electrospray source operated in the positive-and negative-ion modes. The software interface was Xcalibur (version 2. 1) (Thermo Fisher Scientific). The mass spectrometer was calibrated before each analysis using a calibration solution provided by the manufacturer (external calibration). The UHPLC chromatographic separation was performed on a Hypersil GOLD C18 1. 9 lm, 2. 1 9 150 mm 2 column (Thermo Fisher Scientific) equipped with an online prefilter (Interchim, Montlucon, France). The mobile phases were (A) 100 % water and (B) 100 % acetonitrile with 0. 1 % formic acid. After an isocratic step of 2 min at 100 % phase A, a linear gradient from 0 to 100 % B was run over the next 11 min with a mobile phase flow of 500 ll/min.
Data processing Automatic peak detection and integration were performed using XCMS software package  (Smith et al. 2006 ). Raw files were preliminarily converted to netCDF format with Xconvert (Thermo Scientific, Les Ulis, France). Data were processed using XCMS version 1. 14. 1 running under R version 2. 8. 1. The R language was installed on a Dell Eight-core Intel Xeon 3. 00 GHz Processor with 16 Go RAM running Linux (Centos 5. 2 x86_64). The matchedFilter algorithm was used and default values were set for all parameters except for fwhm, step, steps, mzdiff, and mzwid, which were set respectively at 4, 0. 01, 2, 0. 01, and 0. 01 (for both grouping steps).
Statistical analysis Univariate correlation with time storage duration was tested by using the nonparametric Spearman coefficient of rank correlation  (Kvam and Vidakovic 2007)  and controlling the false discovery rate  (Benjamini and Hochberg 1995 ) at a 0. 01 threshold. All peak intensities of the metabolites showing significant variations were confirmed by manual integration using the QuanBrowser software, in order to avoid any false-positive or falsenegative deconvolution that may have been generated with XCMS. Prior to multivariate analysis, one observation from Exp. 2 with outlier distribution quantiles  (Alonso et al. 2011 ) (p = 10 -3 threshold) was discarded. Intensities were log 10 transformed and variables were mean-centered and unit-variance scaled, prior to principal component analysis (PCA) and partial least-squares (PLS) analysis with two components (with the storage duration as Y response).
To assess the predictive performance of each PLS-DA model, cross-validation was used to compute the diagnostic Q 2 value  (Wold et al. 2001). The statistical significance of Q 2 was estimated by permutation testing  (Szymanska et al. 2012 ): the Q 2 value was compared to the distribution of 10 3 Q 2 values corresponding to models built after random permutation of the response labels. Normalization, univariate and multivariate statistical analyzes were all performed using scripts coded and run in the R language and environment (R Core Team, 2013; version 3. 0. 2). Venn diagrams were drawn using the ''Vennerable'' package version 1. 0 (Swinton 2009).
Metabolite identification Metabolites were identified by taking into account the metabolomics standard initiative (MSI) criteria  (Sumner et al. 2007 ) and by using a spectral database developed in our laboratory  (Roux et al. 2012). UHPLC/HRMS-peaklists generated by XCMS were annotated using an inhouse software tool that matches masses (within a 5 ppm range) and retention times of ions of biological datasets with those of reference compounds contained in the spectral database.
Recording of NMR spectra human urine Proton NMR (1H NMR) spectra were acquired with a Bruker Avance-500SB (Bruker, Wissembourg, France) operating at 500. 13 MHz and equipped with a 5 mm broadband inverse probe with non-spinning samples and thermostated at 298 K. Spectra were collected into 32 K points with a spectral width of 5,000 Hz (10 ppm). Acquisition of the spectra was performed with a presaturation of the water signal during 2 s after a relaxation delay of 8 s. A total of 128 transients were accumulated for a total acquisition time of 28 min and 24 s. Spectra were phased, baseline corrected, and referenced to TSP manually with the XWIN-NMR software (version 3. 5, Bruker, Karlsruhe, Germany).
Processing of NMR data For PCA, spectra were transformed into a table of buckets of size 0. 02 ppm with the AMIX software (Version 3. 9. 7; Bruker Biospin, Germany). PCA were performed with the SIMCA-P software (version 11. 0. 0. 0; Umetrics, Sweden). The spectral region from 4. 50-5. 50 ppm was excluded to remove variability due to suppression of the water resonance signal. Each bucket was divided by the total integral of all individual regions to normalize the urine dilution between individual samples. Spectral regions were investigated to identify the discriminating metabolites. Spectra were processed and analyzed with MestReNova sofware (version 6. 2. 0, Mesrelab, Santiago di compostella, spain). Spectra were obtained after a Fourier transformation without apodization. After phase correction and baseline correction, peak areas were measured by line fitting.
LC-MS metabolomics analysis of concentration variations induced by urine collection and storage conditions Preprocessing of the raw LC-MS files (XCMS peak detection and retention time alignment, followed by normalization and QC filtering; see the experimental section for detailed information) resulted in 4 peak Tables (2 experiments 9 2 ionization modes) with a number of features ranging from 1,476 to 4,164, which were further analyzed by unsupervised and supervised multivariate modeling. Unsupervised (PCA) and supervised (PLS) multivariate analyses of the whole datasets were performed, respectively, in order to have an overview of the overall variability, and to look for specific trends according to collection condition and storage duration. Exp. 2 was analyzed first because it exhibited the highest bacterial growth (Fig.
1 did not provide significant PLS modelling, suggesting that time dependence may be masked by other sources of variability in this particular dataset. In conclusion, a discrimination between the samples stored at room temperature and those stored at 4 °C is evidenced in both experiments. To further characterize the human urine metabolites impacted by collection conditions, we focused on the molecules that were identified by using our spectral database, on the basis of accurate mass, chromatographic retention times and MS/MS  (Roux et al. 2012). Multivariate analyses on these reduced datasets showed similar patterns compared with the original data (figure provided as supplementary material 8), suggesting that restriction to identified metabolites did not introduce any major bias.
We thus recruited 142 children (71 UU, 71 EU) to serve as non-matched controls for RBL tests, of whom seven were lost to follow-up. The characteristics of the 168 children assessed on RBL scales are described in table 1 and in table  S3. Mothers of EI children were less well educated and were more likely to live alone than mothers of EU children and far less than UU mothers  [12] , result shifted by a repartition bias favoring the participation of better educated mothers in the control group (differential selection of EU and UU neonates according to willingness to participate). That is why, confronted with overt social determinism of CHIKV infection  [20] [21] [22] , we then used a propensity scale of social risk of infection to control the role of maternal social characteristics on DQ while canceling repartition bias (data not shown).
Acknowledgments We are indebted to  Guy Chauvet ,  Alain Fite ,  Florence Lavaud ,  Michel Renouil ,  Martine Robillard ,  Sandrine Terrentroy ,  Frederic Villeroy  and the staff of the  CIC-EC of Reunion island. We are grateful to  Dr Marie Balu  for additional neuroradiologic follow-up and  Denis Lamblin  for sensorineural testing and additional examination in CAMSP centers. We also thank  Andrew Hobson ,  Pr Denis Malvy ,  Pr Dominique Gendrel  and  Pr Ge  ´rard Breart for helpful discussions and critical reading of the.
Together, Eqs. 4 and 1 allow computing the effect of the torsional coupling on the expression of a pair of genes, as a function of their distance, promoter strength and the basal superhelical level (Fig. 3 ). We integrated the model numerically with an iterative algorithm. Starting from the transcription rate in absence of local supercoiling (k 0 ) for both genes, the procedure successively adjusts the level of supercoiling (and thus the transcription level) of each promoter until numerical convergence (fixed point). This procedure, as well as all analysis and plotting, were implemented in Python, with the Numpy/Scipy  [51]  and MatPlotLib  [52]  libraries.
Acknowledgments We thank the  ''Stochagene ''  group  project for discussions, in particular  O. Gandrillon  and  G. Kaneko ;  S. Reverchon ,  W. Nasser , and  R. Everaers  for discussions;  M. Se  ´mon and  L. Duret  for advice on genomic data analysis;  H. Berry  and  O. Gandrillon  for critical reading of the manuscript.
Skimmed milk samples were diluted at 1:100 in SRID buffer (ID Biotechnologies) and loaded (15 ml/well) onto RID plates containing agar gel with anti-alactalbumin antiserum. The plates were incubated in a humidified atmosphere at 37uC for 48 h. The plates were then incubated with 2% acetic acid for 2 min and rinsed with water. Images were captured using the IDRing Viewer (ID Biotechnologies) and the precipitation rings were analysed with IDRing Meter software (ID Biotechnologies).
Histology For histology, the mammary specimens were fixed in RCL2 (Excilone, Elancourt, France) for 24 hours at 4uC. They were then dehydrated in 70% ethanol and embedded in paraffin according to standard histological protocols. Five micrometer sections, lying at least 100 mm apart, were mounted on slides. The slides were stained with Hematoxylin-Eosin-Safran (Sigma, Saint Quentin Fallavier, France) and then digitalized using a scannerNanoZoomer (Hamamatsu Photonics, Hamamatsu City, Japan). The latter technique enabled observation of the entire section. Five sections per cow were processed and the images were analysed using both the Hamamatsu NanoZoomer Digital Pathology Virtual Slide Viewer and Image J software (Wayne Rasband; National Institute of Health, Bethesda, MD).
Electron microscopy Specimens for transmission electron microscopic observations were fixed with 2% glutaraldehyde in 0. 1 M sodium cacodylate buffer, pH 7. 2, for 4 hr at room temperature, and then post-fixed with 1% osmium tetroxide containing 1. 5% potassium cyanoferrate, gradually dehydrated in ethanol (30% to 100%), and embedded in Epon (Delta Microscopy, Ayguesvives, France). Semi-thin sections (1mm) were collected onto glass slides, counterstained with methylene blue-Azur II and imaged on a Leica Leitz DMRB microscope equipped with a DP50 camera (Olympus) coupled to Cell software (Olympus). Thin sections (70 nm) were collected on 200-mesh copper grids, and counterstained with lead citrate before examination with a Zeiss EM902 electron microscope operating at 80 kV (Carl Zeiss, France).
Images were acquired with a charge-coupled device camera (Megaview III) and analysed with ITEM Software (Eloı ¨se, France) at the MIMA2 facilities (www. 6. jouy. inra. fr/mima2, UMR1313 Genetique Animale et Biologie Integrative, INRA, Plateau de Microscopie Electronique, F78350 Jouy-en-Josas, France).
40 bp single reads were generated on an Illumina Genome Analyzer IIx, using the TruSeq SR Cluster Kit v2-cBot-GA and TruSeq SBS Kit v5. Read quality was checked with FastQC in the ng6. toulouse. inra. fr environment  [21]. The reads were then spliced-aligned to the bovine reference genome (UMD3. 1) using the TopHat v2. 0. 5 software package  [22]. The resulting bam files were processed with a modified version of CUFFLINKS 2. 0. 0  [23] , called sigcufflinks, which also produces raw read counts per transcript (available upon request at sigenae. org), in order to discover genes and quantify transcripts. The quantification results were merged in the expression table. The merged GTF file was also used to extract the fasta sequence of the newly discovered transcripts so that they could be annotated.