dataesr/scientific-paragraphs-categorization huggingface dataset:
2011; Junot et al. 2014 ) and facilitate the detection of minor lipid species. Furthermore, LC also enhances the separation of isomeric lipid species  (Lee et al. 2011; Bird et al. 2012 ). These LC-HRMS-based global methods enable to achieve a broad lipidome coverage with detection and relative quantification of several hundred unique lipid species in a single experiment  (Gregory et al. 2013). However, dedicated informatic tools are required in order to handle of the huge quantity of data generated by LC/HRMS systems. Before automatic detection and annotation, raw data must be converted into data formats compatible with peak detection and alignment software tools such as MZmine  (Katajamaa et al. 2006)  or XCMS  (Smith et al. 2012). Thanks to accurate mass measurements provided by HRMS, lipidomic features can be annotated by using lipid databases such as that of the LIPID MAPS consortium  (Fahy et al.
2005) , which introduced the ''Comprehensive Classification System for Lipids''. This classification system aims to catalog lipid species and makes available online tools to support lipid identifications. More recently, the LipidBlast in silico tandem mass spectral database has been implemented and covers compounds of 26 lipid classes  (Kind et al. 2013). In parallel, manufacturers have also developed commercial software tools, such as Lipid Search from ThermoFisher Scientific  (Taguchi and Ishikawa 2010) , Lipid View from AB Sciex  (Ejsing et al. 2006)  or SimLipid from Premier Biosoft, for direct annotation from raw data. However, reliable lipidomic data treatment workflows able to handle the detection and alignment of features, together with selection and annotation of analytically reliable ones, are still emerging.
Liquid chromatography The reversed phase chromatographic conditions were adapted from  Hu et al. (2008). Methanol was preferred to acetonitrile since it has been observed that it decreased the carryover of most apolar lipid species (data not shown). CSF total lipid extracts were separated on an Dionex Ultimate 3000 UPLC system (Thermo Scientific, San Jose, CA) using a kinetex C 8 150 9 2. 1 mm, 2. 6 lm column (Phenomenex, Sydney, NSW, Australia). Mobile phase A consisted of H 2 O/MeOH 60/40 (v/v) and 0. 1 % formic acid and mobile phase B of IPA/MeOH 90/10 (v/v) and 0. 1 % formic acid. Ammonium formate (10 mM) was added to both mobile phases in the positive ion mode. The presence of ammonium salt in mobile phases decrease drastically the detection of sodium adducts which now constitute minor lipid species in the positive ion mode (data not shown).
Data processing Raw files were first of all converted to mzXML format with MSconvert (ProteoWizard). Peak detection, alignment and integration were performed with the XCMS software package version 1. 30. 3  (Smith et al. 2006)  including the CentWave algorithm  (Tautenhahn et al. 2008). The list of the parameters used is available as supplementary material (see Table  S2  provided as supplementary material). A data matrix was generated by the software. It contained the list of all detected features, including information such as accurate measured masses, retention times and areas of chromatographic peaks.
Results and discussion We first of all report on the development and validation of a lipidomic method based on reversed phase liquid chromatography coupled to high-resolution mass spectrometry (RPLC-HRMS). A data processing workflow was also developed, including automatic peak detection and alignment using the XCMS package  (Tautenhahn et al. 2008; Smith et al. 2012) , several filtration steps in order to obtain a list of reliable features, and automatic feature annotation which was achieved thanks to an in silico lipid database developed in our laboratory (see Table  S5  in supplementary material) and based on accurate measured masses, retention time windows and relative isotopic abundance (RIA) of lipid species. This lipidomic approach was then used to analyze CSF samples collected from 43 individuals.
Despite of the use of ammonium formate, sodium adducts ([M ? Na] ? ) were detected for some lipid classes, i. e. , Cer, HexCer, SM, LPC, PC, glycerolipids (DG and TG) and cholesteryl esters. Choline containing lipid classes (SM, LPC and PC) were also detected in the negative ion mode as formate adducts ([M -H ? CHO 2 ] -). Finally, water losses in the positive ion mode ([M ? H -H 2 O] ? ) were observed for Cer, HexCer and DG lipid standards, as already described  (Christie and Han, 2010). All these pseudo-molecular, adducts ions and in source fragments were added in the in silico database to improve annotation confidence (see Table  S5  in supplementary material).
The main CentWave parameters, i. e. , the range of chromatographic peak width and the signal-to-noise threshold for validation of features, were carefully optimized by comparing false-negative and false-positive rates for manually annotated datasets. The reliability of the XCMS software in term of peak integration was evaluated using chromatographic peaks related to internal standards and endogenous lipids from CSF samples spiked with the IS mixture. Areas of 164 chromatographic peaks (including 110 annotations corresponding to internal standard signals and the 54 endogenous lipids), ranging between four orders of magnitude, and automatically detected by XCMS were compared with those integrated by using Xcalibur (Thermo Fisher scientifics, Courtaboeuf, France) via the QuantBrowser module (ICIS algorithm).
8, (ii) the coefficients of variation of the areas of chromatographic peaks of features in QC samples should be less than 30 %, and (iii) the ratio of chromatographic area of biological to blank samples should be above a value of 1. 5. Feature annotation was then achieved through the implementation of an in silico database which contains 28,807 exact masses corresponding to pseudo-molecular ions ([M ? H] ? , [M -H] -and [M -2H] 2-), adducts ([M ? NH 4 ] ? , [M ? Na] ? , [M -H ? CHO 2 ] -), and in source fragments ([M ? H -H 2 O] ? ) ions of the five major lipid families indexed in the LIPID MAPS library, along with their corresponding 13 C and double 13 C isotopes. Furthermore, specific retention time windows for each lipid class were also added by examining retention times of species containing the longest and the shortest fatty acyl chains (see Table  S4  in supporting information).
Thanks to scripts developed in R language, exact masses of XCMS detected features were matched to those of our in silico database (±4 ppm). Annotations were kept if the retention times of the corresponding features were within the retention time window specific of their putative lipid class. However, accurate measured masses and retention time windows were not sufficient to assess the relevance of lipid species, and additional spectral information was required to confirm annotations  (Kind and Fiehn 2006; Erve et al. 2009; Xu et al. 2010). Annotated lipid species were thus kept (i) if their 13 C isotope was detected and aligned in time (±5 s) and (ii) if all annotated signals corresponding to the same lipid species (i. e. , pseudo-molecular ions, adduct ions and/or in source fragments, either as monoisotopic or 13 C and 29 13 C isotopes) must have the same retention time as a reference ion (±5 s, and ±10 s between two ionization modes after merging corresponding peaktables).
Annotation of the human CSF lipidome The RPLC-HRMS based lipidomic method and its associated data processing workflow were applied to the analysis of 43 human CSF samples. From complex LC/ HRMS raw data, 51,449 features were generated in combined positive and negative ion modes by the XCMS/ Centwave process, among which 32,478 were found to be analytically relevant (i. e. , satisfaction values including dilution factors of coefficient variation between QC samples and ratio of chromatographic area of biological to blank samples). Then, 771 features were annotated, corresponding to pseudo-molecular, in-source fragment and adducts ions, as shown in Table  2. Of these annotations, 550 features were attributed to unique lipid species. This result emphasizes that many reliable features are not annotated by our in silico database and remain to be identified.
MS/MS experiments Fig. 2  The data processing workflow. Features were filtered according to the following criteria: (i) the coefficients of correlation between dilution factors of QC samples (by factors of 1, 2, 4 and 8) and areas of chromatographic peaks (above 0. 8), (ii) the coefficients of variation of areas of chromatographic peaks related to features detected in QC samples (less than 30 %) and (iii) the ratio of chromatographic areas of biological to blank samples (more than a factor of 1. 5). Features were then annotated using a spectral database on the bases of exact masses and retention time windows specific to each lipid family. Each unique lipid species must be detected by XCMS/CentWave with at least its corresponding 13 C isotope, and eventually fragment and adduct ions, which must be eluted at the same retention time as that of the pseudo molecular ion (with a tolerance of ±5 s, and ±10 s after the fusion of the two peaktables obtained in the positive and negative ion modes).
MS/MS experiments improved the annotation of lipid species The reliable characterization of the detected lipid species represents a critical issue for a global lipidomic approach. The implementation of our LC/HRMS method coupled to a data processing workflow enabled the annotation of 771 features corresponding to 550 unique lipid species in human CSF. MS/MS experiments were conducted on a QC sample using inclusion lists in both positive and negative ion modes in order to improve these annotations. The detection of some particular lipid species such as LPC/PC and SM in both positive and negative ionization modes, as protonated ion ([M ? H] ? ) and formate adduct ([M -H ? CHO 2 ] -), enabled to confirm their annotation and also improved their structural characterization. This is for example displayed in Fig.
According to the metabolomics standards initiative (MSI), annotations in untargeted lipidomic studies correspond to putatively annotated compounds (level 2 of identification) or putatively characterized compound classes (level 3 of identification)  (Sumner et al. 2007 ). In addition, the high level of structural precision provided by public lipid databases cannot be achieved using a single analytical method, (e. g. , double bonds localization or cistrans isomerism) and without appropriates internal standards. When compared to previous studies conducted in human CSF samples using ESI mass spectrometry, most of the major lipid families such as glycerophospholipids  (Mudler et al. 2003; Kosicek et al. 2010; Kosicek et al. 2012; Fonteh et al. 2013) , sphingomyelins, sterols and free fatty acids  (Tourtellotte 1959) , that are usually analyzed by several complementary targeted methods, were detected in a single experiment thanks to our global approach.
Conclusion We have developed and validated an untargeted lipidomic approach enabling to profile, annotate and characterize numerous lipid species, including isobars and isomers, in human CSF samples. Our method enabled the detection of more than 30,000 features suitable for statistical analysis, among which 550 were annotated as unique monoisotopic lipid species using annotation tools. Thanks to tandem mass experiments, 304 annotations of distinct lipid species were improved in both positive and negative ion modes. Finally, our study provides an extensive database of the human CSF lipidome which is available to the scientific community. It constitutes a relevant foundation for future studies aimed at finding biomarkers of neurological disorders.
Acknowledgments This work was supported by  MetaboHUB  ( ANR-11-INBS-0010 ) and the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement number  2012-305121  ''Integrated European -omics  research project for diagnosis and therapy in rare neuromuscular and neurodegenerative diseases (NeurOmics) ''. The authors thank  Drs. F. Sedel ,  F. Mochel  and  F. Lamari  for providing CSF samples and for valuable discussions.
The potential of data-driven docking is reflected in the success of the HADDOCK server and software in recent CAPRI experiments (Critical Assessment of Protein Interaction)  [14, 15] , as well as in the number of structures deposited (N 120) in the Protein Data Bank (PDB), which were calculated using our software. Five years ago, we introduced the HADDOCK Web server to provide a user-friendly interface to the software and streamline its usage by non-expert users in the structural biology field  [16]. Shortly after, it was updated to handle multi-body docking  [17]. The development of new and improved protocols and the inclusion of additional sources of restraints culminated in the recently released version 2. 2 of the software, followed by an update of the Web server interfaces. Throughout the next section, we will provide an overview of the newly updated HAD-DOCK Web server and discuss the most relevant additions ‡.
Overview and advances The HADDOCK Web server was created to facilitate the use of our docking software, by removing the burden of its installation and setup, as well as by providing validation routines for input data and options. In addition, since HADDOCK runs are computationally demanding, the Web server offers the users access to sufficient resources-our local clusters-to complete their runs within a few hours. A grid-enabled version of the server can be accessed via the WeNMR Web site §  [18] , which uses resources provided by the European Grid Initiative (EGI) and the associated National Grid Initiatives. This setup, which currently handles most submissions, provides more than 110,000 CPU cores distributed over 41 sites worldwide ||. The HADDOCK Web server aggregates seven different interfaces, each associated with a different level of control over the docking protocol reflected by the number of parameters that can be changed.
Finally, the Web server also offers a File Upload Interface to allow the user to upload a run parameter file, created upon successful validation and submission to the queue, and thus easily redo a docking run or re-run it with slight changes in the parameters. At submission time, once the input data have been properly validated, the server offers the option to download a parameter file and provides a link to the results page, which is also e-mailed to the user. Users are encouraged to save the parameter file since it contains all required input data and settings to reproduce the docking, as recommended in the "Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop" (Recommendation 1)  [25]. The results page allows monitoring of the progress of the docking run. After a successful docking run, the user will receive another e-mail redirecting him/her to the updated results page (see Fig.
Acknowledgements The development of HADDOCK and its Web server has been supported by grants from the  Netherlands Organization for Scientific Research  ( VICI grant  no. 700. 96. 442  and  ECHO  grant no. 711. 011. 009 ) and by  European FP7  and  H2020  e- Infrastructure grants (e-NMR  grant no. 213010 ,  WeNMR  grant no. 261572 ,  EGI-Engage  grant no. 654142  and  INDIGO-DataCloud  grant no. 653549 ). The  EGI ¶ through the national GRID Initiatives of Belgium, France, Italy, Germany , the  Netherlands, Poland, Portugal, Spain, UK, South Africa, Malaysia and Taiwan ; the  Latin America GRID Infrastructure  via the  Gisela  Project; the  International Desktop Grid Federation  with its volunteers and the  US Open Science Grid  are acknowledged for the use of Web portals, computing and storage facilities.
Background: Several tools are available for visualizing genomic data. Some, such as Gbrowse and Jbrowse, are very efficient for small genomic regions, but they are not suitable for entire genomes. Others, like Phenogram and CViT, can be used to visualise whole genomes, but are not designed to display very dense genomic features (eg: interspersed repeats). We have therefore developed DensityMap, a lightweight Perl program that can display the densities of several features (genes, ncRNA, cpg, etc. ) along chromosomes on the scale of the whole genome. A critical advantage of DensityMap is that it uses GFF annotation files directly to compute the densities of features without needing additional information from the user. The resulting picture is readily configurable, and the colour scales used can be customized for a best fit to the data plotted.
Results: DensityMap runs on Linux architecture with few requirements so that users can easily and quickly visualize the distributions and densities of genomic features for an entire genome. The input is GFF3-formated data representing chromosomes (linkage groups or pseudomolecules) and sets of features which are used to calculate representations in density maps. In practise, DensityMap uses a tilling window to compute the density of one or more features and the number of bases covered by these features along chromosomes. The densities are represented by colour scales that can be customized to highlight critical points. DensityMap can compare the distributions of features; it calculates several chromosomal density maps in a single image, each of which describes a different genomic feature. It can also use the genome nucleotide sequence to compute and plot a density map of the GC content along chromosomes.
Implementation DensityMap is run with Perl script in the command line and uses the GD::SVG Perl package to produce SVG pictures. DensityMap computes a representation of the density of a feature on chromosomes using one GFF file (GFF2, GFF2. 5 or GFF3) describing a chromosome as input. The program plots as many density maps along a chromosome as there are features specified. It can plot a density map for the plus strand, minus strand, or the plus and minus strands, combinations of plus and minus strands, or plus, minus and compiled strands, for each feature. Density is computed using a tilling window without overlap whose length is fixed by the user or automatically computed to produce an output image that fits the maximum image size. All this information can be set by the user in the command line.
DensityMap also automatically calculates the density of a feature for each pixelized region of a chromosome, whatever the representation scale used. The way the density of a feature varies along a chromosome is represented using a colour scale from 0 to  This makes it easy to edit high quality images for publication. The program also includes graphical options for configuring almost all elements (margins, map width, scale, etc. ) of the image. The options are shown in Table  1. The program computes the size of the output image according to the number of chromosomes (GFF files), the number of features to represent, the number of strands to plot and the window size. If the user chooses automatic scale computing, the program calculates a windows size that gives an image that lies within the maximum image size defined by the user.
The program asks the user to check the output picture size before processing the data. It then builds the image by adding the various graphical elements (background, title, scale) and processes the data for plotting the chromosome strands. It sequentially opens GFF files, filter features (GFF file third column) selected by the user with the option -ty (types). The intervals are collected and sorted by their beginnings and merged to remove overlaps. Lastly, the program computes the densities -(number of bases  covered by the feature /window size) x 100 -and then draws it within the image. A synopsis of the main algorithm and functions is supplied in Additional file 1 and a manual in Additional file 2. 2L LI N E -+ 2L LT R -+ 2L R C -+ 2R LI N E -+ 2R LT R -+ 2R R C -+ 3L LI N E -+ 3L LT R -+ 3L R C -+ 3R LI N E -+ 3R LT R -+ 3R R C -+ 4 LI N E -+ 4 LT R -+ 4 R C -+ X LI N E -+ X LT R -+ X R C -+ Y LI N E -+ Y LT R -+ Y R C -+ Even if the main purpose of DensityMap is to plot whole genome data, it can be interesting to compare specific loci of several sequences.
It is also very efficient for plotting density maps of total repeatssatellites, TEs, simple sequence repeatsof human genome -5 295 850 featuresin 2 min 14 second a on computer equipped of a Intel(R) Xeon(R) W3670 CPU @ 3. 20GHz and 16 Go of RAM. DensityMap is very simple to install and run, and so is a good way to obtain a global view of genomic data. To make easier the usage of DensityMap to persons non initiate to linux command line, we developed a web graphical user interface for online DensityMap analysis.
Horizontal subsurface Flow Constructed Wetlands (HF CWs) are biofilters planted with aquatic macrophytes within which wastewater is treated mostly through contact with bacterial biofilms. The high concentrations of organic carbon and nutrients being transported leads to high bacterial biomass production, which decreases the flow capacity of the porous material (bioclogging). In severe bioclogging scenarios, overland flow may take place, reducing overall treatment performance. In this work we developed a mathematical model using COMSOL Multiphysics T M and MATLAB R to simulate bioclogging effects in HF CWs. Variably saturated subsurface flow and overland flow were described using the Richards equation. The growth of bacteria was described using a Monod equation, and its effects on the hydrodynamics were taken into account by using a conceptual model that adapts the value.
Model Implementation Figure  1  shows the steps taken during the numerical solution of the model equations presented in previous sections. All equations were implemented using the COMSOL Multiphysics TM v4. 3b interface, except for the equations used to calculate the relative hydraulic conductivity in the presence of biofilm (k r ) and the volumetric water content (θ) (Eqs. 10 and 11), that were implemented in MATLAB R. The exchange of data between COM-SOL Multiphysics TM and MATLAB R is made at each numerical iteration using the Livelink for MATLAB R COMSOL Multiphysics TM module.
The contribution of the thermal scattering law of hydrogen in light water to isothermal temperature reactivity coefficients for UOX and MOX lattices was studied in the frame of the MISTRAL critical experiments carried out in the zero power reactor EOLE of CEA Cadarache (France). The interpretation of the core residual reactivity measured between 6 °C to 80 °C (by step of 5 °C) was performed with the Monte-Carlo code TRIPOLI4 ®. The nuclear data from the JEFF-3. 1. 1 library were used in the calculations. Three different thermal scattering laws of hydrogen in light water were tested in order to evaluate their impact on the MISTRAL calculations. The thermal scattering laws of interest were firstly those recommended in JEFF-3. 1. 1 and ENDF/B-VII. 1 and also that recently produced at the atomic center of Bariloche (CAB, Argentina) with molecular dynamic simulations.
The latter is given by the derivative of the difference Dr(T) between the calculated (C) and measured (E) excess of reactivity with respect to the temperature: Da iso ðT Þ ¼ ∂DrðT Þ ∂T ; ð1Þ with DrðT Þ ¼ r C ðT Þ À r E ðT Þ: ð2Þ A series of MISTRAL experiments  [7] [8] [9] [10] [11] [12] [13] [14] [15]  was carried out in the EOLE facility of CEA Cadarache (France) in order to study Da iso for UOX (MISTRAL-1 configuration) and MOX (MISTRAL-2 and MISTRAL-3 configurations) lattices. Previous interpretations  [16, 17]  were performed with the deterministic code APOLLO2  [18]  by using the evaluated nuclear data libraries JEF-2. 2 and JEFF-3. 1. 1. Results are summarized in Table  1. According to conclusions reported in reference  [16] , Da iso is mainly sensitive to the spectral shift of thermal neutrons in the low temperature range (T < 40 °C).