dataesr/scientific-paragraphs-categorization huggingface dataset:
Elimination of the fluorescent signal from adsorbed NPs by Imaging flow cytometry 100 nm-Por-SiO 2 were tightly attached to the cell membrane after thorough rinsing (Figure  2A-C ) and fluorescence from adsorbed NPs could not be eliminated efficiently by classic fluorescence quenchers. We thus used ImageStream X platform which is an imaging flow cytometer that combines the speed, statistical power and fluorescence sensitivity of flow cytometry with the functional performance of high resolution microscopy. Powerful image analysis software allows quantification of the fluorescence at different cellular localizations. A mask representing the whole cell was defined by the bright-field image, and an internal mask was defined by eroding the whole cell mask by 3 μm (6 pixels) in order to eliminate the fluorescent signal coming from NPs attached to the cell surface thus measuring only the internalized part.
This might be due to the saturation of uptake mechanisms as the IS also decreased with increasing concentrations after 4 and 24 h of treatment. Conversely, at lower doses NPs continued to be efficiently taken up until 24 h of treatment. Using these two parameters (IS and MFI inside the eroded cell mask) analyzed by ImageStream X Ideas software we have obtained important information about NP uptake. Comparing IS values after treatment at 37°and 4°C allowed us to define an eroded cell mask to distinguish adsorbed from internalized part. Using MFI inside the eroded cell mask we could establish the time and dose-dependency of SiO 2 NP internalization. Combining these two parameters allowed us to demonstrate the saturation of endocytic mechanisms of NPs.
Imaging flow cytometry analysis Cells were seeded in 6-well plates at 10,000 cells/cm 2 in complete cell culture medium and incubated for 48 h before treatment with 2. 9 mL/well of 100 nm-Por-SiO 2 -NPs at indicated concentrations. At the end of the exposure to NPs, the media was removed, cells were thoroughly washed with PBS and cells were harvested. Cell suspension was centrifuged for 5 min at 200 g at 4°C and the cell pellet was resuspended in 500 μl of 4% para-formaldehyde (PFA, Santa Cruz Biotechnology Inc, Heidelberg, Germany). After 20 min of incubation in PFA, cells were rinsed three times with PBS and finally suspended in at least 50 μl of PBS. At least 2,500 cells were analyzed using Amnis ImageStream x platform (Amnis, Proteigene, Saint Marcel, France) and Inspire TM system software (Amnis).
Camera magnification was 40x, 488 nm excitation laser was at 100 mW and 785 nm excitation laser was at 2. 33 mW, except for non fluorescent NPs where it was set at 0. 01 mW. The images were acquired with a normal depth of field, providing a crosssectional image of the cell with a 4 μm depth of focus. A mask representing the whole cell was defined by the bright-field image, and an internal mask was defined by eroding the whole cell mask by 6 pixels (equivalent to 3 μm, as the size of 1 pixel is 0. 5 μm) in order to eliminate the fluorescent signal coming from the NPs attached to the cell surface thus measuring only the internalized part. The results are analyzed by IDEAS software (Amnis),. Values of the internalization score, mean fluorescence intensity and mean side scatter intensity were calculated for at least 500 cells per sample.
4) and a 1 to 1. 5x zoom. The refractive index of immersion oil was 1. 512. Considering optical laws the theoretical resolution was calculated and instrument settings adapted to obtain the best possible resolution in our images. Image treatment was done with Image J software (Image J 1. 42 NIH, USA). The three-dimensional (3D) structure of the cells treated with NPs was reconstructed from corresponding confocal images using IMARIS software 7. 5 (Bitplane). Pearson's correlation coefficient was calculated using JACoP (Just Another Colocalization Plugin) for images with lateral resolution of 0. 09 μm and axial resolution of 0. 5μm.
Statistical analysis Every experiment was repeated at least twice with duplicates or triplicates of each condition. Data are represented as means ± SD or SEM and were analyzed on commercially available software SigmaStat (version 3. 0, Systat software Inc, San Jose, California, USA) analysis of variance (one-way ANOVA) followed by Bonferroni post hoc test for multiple comparisons with p < 0. 05 (two tailed) considered as significant.
Acknowledgments This study was funded by the  E. C. FP7 ENPRA (n°228789 ) grant. Support for this study was also provided by  Nanotrans  ( N°EST-2010/2/079 ) and TiSiTrans ( N°PNR-EST-2010/2/79 ) grants. We acknowledge the confocal microscope platform in the  Institute Jacques Monod, Paris, France. The authors wish to thank  Dr Tad George  for fruitful scientific discussions in the field of imaging flow cytometry. We acknowledge  Dr Florent Busi  for helping preparation of the manuscript,  Kevin Moreau  and  Emile Petit  for technical assistance and  Pierre Hugounenq  for DLS analysis. We wish to thank  Jonathon Hauser  for proof reading the manuscript.
A. Design / simulation information flow The link between design and simulation is essential to the designers that based their technological choices on simulation analysis. Indeed the bidirectional flow of design and simulation can decrease the number of modifications between CAD and FEM models, decreasing the development time of the validated product model. (Nguyen Van, 2006)  defines architecture to facilitate the collaboration loops between design and simulation. He uses standard format to share information between those phases, ensuring the semantic flow conservation. (Assouroko et al. , 2010)  proposes an approach for managing interoperability software CAD/CAE. To do so, he defines a model based on ontologies. To link the different ontologies, he defines a relations management method and tool.
In fact, According to the Frost and Sullivan research firm  (Keith Robinson, 2002) , "Manufacturing Process Management (MPM) software is its own unique category and provides links between upstream PLM software, such as computer-aided design (CAD) and Product Data Management (PDM), and downstream applications, such as Enterprise Resource Planning (ERP)". In addition, the MPM responds to several challenges faced by business-critical today, including the need to shorten the time-volume, optimize production execution, ensure integration of information, allowing engineers, designers, and corporate staff to work interactively, etc.
Background Non-covalent interactions, such as hydrogen bonds, between ligands and biomacromolecules forming a complex provide important clues in the understanding of biological processes and the design of compounds with desirable binding properties. Schematic 2D diagrams are widely used to visualise the binding interactions of ligands, which can provide important clues about their role and function. A lot of software packages are available for visualising structure in 3D, but only a handful exists for generating 2D protein/ligand interaction diagrams. LIG-PLOT  [1]  is the most commonly used and its successor LigPlot+  [2]  includes a new interface, superposition of related diagrams and links to PyMOL and RasMol. Pose-View  [3]  automatically generates structure diagrams of molecular complexes.
A module of the commercial software MOE -Molecular Operating Environment -  [4]  generates schematic diagrams for protein-ligand complexes, and other commercial software vendors offer similar tools. In this paper, LeView (Ligand-Environment Viewer) is introduced. This new tool automatically detects Correspondence: segolene. caboche@pasteur-lille. fr 1 Protein Data Bank in Europe, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK 2 Present address: IFR142 Molecular and Cellular Medecine, INSERM, CNRS, Institut Pasteur de Lille and Univ Lille Nord de France, Lille, France ligands in a PDB file, generates 2D diagrams for biomacromolecule/ligand interactions and allows the user to customise diagrams through an interactive and intuitive graphical interface. LeView is written in Java and distributed as an executable file (source code is also available under GNU Public Licence, Additional file 1) in two versions: a version with an interactive graphical interface and a command-line version for use in pipelines.
Ligand layout In LeView, ligands are encoded by undirected labelled graphs in which nodes represent atoms, edges correspond to chemical bonds and labels are atom names. A graph mapping algorithm is used to map each ligand residue with the corresponding PDBeChem cif file to obtain the bond order as PDB files do not contain this information. The ligand layout algorithm is then applied to obtain the 2D coordinates. First, the ligand is partitioned into structural elements: rings (the smallest set of smallest rings), chains containing at least 3 connected non-terminal atoms (i. e. C, N, O, S), isolated atoms and terminal atoms. Figure  2a  shows the partitioning of epoxy succinyl inhibitor (EPO) of PDB entry 1CSB  [6]. Once the ligand has been partitioned a set of rules are applied to each structural element in order to obtain an aesthetic diagram, i.
A conflict is detected when the distance between two atoms is less than 0. 4 Å. In this case, all torsion angles which do not imply ring atoms are flipped (0 to 180 degrees) if this change reduces the overlaps in the molecule. An explicit residue, the cystein 29 of chain A (CYS29) is connected to the ligand in the PDB entry 1CSB through a LINK record wich specify connectivity between residues that is not implied by the primary structure. The ligand layout algorithm is applied to the explicit residue CYS29. Once the residue 2D coordinates are obtained, the residue is randomly placed on a circle with a radius of the real 3D distance. The number of bond crossings and atoms overlaps with the ligand are calculated for each position obtained by rotating the residue through 10 degrees each time. The returned position is the position minimising the number of bond crossings and atom overlaps.
A conflict occurs with several atom overlaps and bond crossings. d shows the ligand layout (in black) obtained after the conflict resolution step and the processing of the explicit residue (Cystein 29 -chain A). The explicit residue appears in grey and the connection in red. e is the diagram obtained after the interaction layout algorithm. Hydrogen bonds are in blue and nearby residues are represented by ovals coloured depending on hydrophobic properties (polar residues in pink, non-polar in green and other in grey). For each residue the chain ID encoding in the PDB file is given in brackets. f is the final diagram produced by LeView, containing the residue names. elements are processed. Figure  3d  shows the diagram produced by LeView for the gramicidin S (1TK2 PDB entry) containing a macro-cycle.
Text layout The finally step of the layout algorithm is to add text on the diagram. The name of each residue component of the ligand and the name of explicit residues are placed on the diagram in order to minimise overlaps with other diagram elements. The residue name is randomly placed on a circle with the centre being the centroid of the residue which is the arithmetic mean position of all the residue atoms and a radius of Max(distance(centroid, i)) + 0. 5 with i the residue atoms, i. e. the distance between the centroid and the further residue atom plus 0. 5 Å. The number of nearby elements approaching within a distance less than 2 Å is calculated for each position obtained by rotating the text through 10 degrees each time. The position minimising the number of nearby elements is kept. Figure  2f  shows the final diagram generated with default settings by LeView for EPO in the PDB entry 1CSB.
Results and discussion LeView automatically detects biomacromolecules, ligands and metal ions in a PDB file and shows the user a complete list of these. Composite ligands can also be user specified by entering the appropriate residue range. It is possible to create an interactive 2D diagram for each ligand and metal ion and this shows ligands, metals, hydrogen bonds and nearby residues. Nearby residues are defined as biomacromolecule residues (i. e. in proteins, DNA or RNA) which have at least one heavy atom approaching within a cut-off distance to the ligand. LeView allows the user to display several ligands and metal ions from the same or from several different PDB entries, which makes it easy to compare Figure shows the 2D diagram produced by LeView using default settings for EPO in PDB entry 1CSB.
The colour of every element in the diagram can be changed via the colour menu. Hydrogen bonds can be represented by solid lines (default) or arrows from donor to acceptor, and hydrogen-bond distances additionally displayed. LeView allows the user to reposition all the graphical elements in the diagram at will. If desired, individual hydrogen bonds and nearby residues can be deleted from the diagram, by a right click on it. Finally, the diagram can be exported in a variety of raster and vector graphics formats: PNG, GIF, JPG, PDF, SVG and EPS. For the latter three formats, LeView uses the Vec-torGraphics package of the FreeHEP Java Library (http:// java. freehep. org). The list of the interactions represented in the diagram, with the atoms involved and the associated distance, can also be exported as a flat text.
Contrary to other tools, LeView pays a special attention to macro-cycles makes the resulting diagram very clear as shown in Figure  3d. The graphical user interface implemented in LeView makes it very easy to use and allows the user to customise the diagram. Figure  4  is an example of user-customised diagram produced by LeView for the the PDE5 inhibitor sildenafil (PDB entry 1UDT  [8] ). Hydrogen bonds are represented by arrows and the distance is http://www. jcheminf. com/content/5/1/40 ) after user customisation. The ligand appears in black. Hydrogen bonds are represented by blue arrows from donor to acceptor and the distance is displayed. Nearby residues are represented by ovals coloured depending on charge properties (non-polar residues in grey, acidic residues in red, basic residues in blue and uncharged polar residues in purple).
Two water-mediated hydrogen bonds appear in green. The number of water molecules is indicated by the number of H2O. http://www. jcheminf. com/content/5/1/40 shown. LeView is the only program that can display watermediated hydrogen bonds through the graphical interface. Two of them are shown in Figure  4. LeView software uses the Java compile once and run everywhere paradigm and can produce high-quality and clear diagrams which are useful and easily understood by scientist without an expert knowledge of structural biology or chemistry.
Acknowledgements The author gratefully acknowledges the  EBI  for funding and Protein Data Bank in Europe (PDBe; http://pdbe. org)staff for help, testing and discussions. The author thanks  Gerard Kleywegt  and  Tom Oldfield  for advice and for manuscript proof reading and  Roman Laskowski  for his help in ligand detection from PDB files. This work was supported by the  European Bioinformatics Institute , part of the EMBL. Funding for publication charge: IFR142.
Polyphenols are a major class of bioactive phytochemicals whose consumption may play a role in the prevention of a number of chronic diseases such as cardiovascular diseases, type II diabetes and cancers. Phenol-Explorer, launched in 2009, is the only freely available web-based database on the content of polyphenols in food and their in vivo metabolism and pharmacokinetics. Here we report the third release of the database (Phenol-Explorer 3. 0), which adds data on the effects of food processing on polyphenol contents in foods. Data on >100 foods, covering 161 polyphenols or groups of polyphenols before and after processing, were collected from 129 peer-reviewed publications and entered into new tables linked to the existing relational design. The effect of processing on polyphenol content is expressed in the form of retention factor coefficients, or the proportion of a given polyphenol retained after processing, adjusted for change in water content.
Retention factor is defined as concentration of polyphenol in processed food concentration of polyphenol in raw food Â yield factor where yield factor ¼ weight of the food after processing weight of the food before processing The yield factor takes into account change in weight of the food due to processing. In many cases, heat from processing causes loss of water, although some foods, such as rice, absorb water when boiled or steamed. A retention factor for a given polyphenol is specific to both the process and the food. Published data on polyphenol retention factors are numerous, although scattered until now across a large number of publications and not easily available from purpose-built databases. Few retention factor databases have been developed for food nutrients; the United States Department of Agriculture (USDA) Since its launch in 2009, the first release of Phenol-Explorer (Phenol-Explorer 1.
0) has aided estimations of polyphenol intake by generating an instant estimate of the polyphenol composition of a queried food, based on data from as many quality-assessed publications as possible  (15). Similarly, it was possible to search for a food polyphenol, and retrieve all foods in which this polyphenol had been identified, along with average concentrations. A second update, launched in 2011 (Phenol-Explorer 2. 0)  (16) , added data on the metabolism and pharmacokinetics of polyphenols, compiled from >200 dietary intervention studies performed with humans or experimental animals. As a comprehensive polyphenol knowledge base, the database has become a unique and essential tool for polyphenol scientists. Although many studies have determined polyphenol losses from specific foods after processing, no method has hereto been available to calculate, collate and aggregate polyphenol retention factors.
Compilation of data A systematic search was performed for literature on the effects of processing on the polyphenol contents of foods, published up until 1 April 2012. Searches were made in turn for foods from seven food groups (alcoholic beverages, cereals and cereal products, fruits and fruit products, nonalcoholic beverages, oils, seeds or vegetables), consistent with the ontology developed for the first release of Phenol-Explorer  (15). Search terms for individual foods (e. g. 'spinach') were combined with those for specific polyphenols ('ferulic acid') or polyphenol classes (e. g. 'flavonoid OR phenolic acid') and some keywords related to quantitative information (e. g. 'content OR determine* OR quantif*') and food processing (e. g. 'cook* OR process* OR fried OR frying OR boil* OR steam* OR stor*').
All searches were made using the Thomson Reuters Web of Knowledge Science Citation Index Expanded (1945-present). The publications retrieved were compiled and organized using Endnote (Thomson Reuters, New York, NY). In addition, publications used for the first release of Phenol-Explorer (polyphenol composition of foods only) were checked for relevant food processing data, and their reference lists checked for other relevant publications. Titles and abstracts were screened and any non-relevant publications eliminated. Of the remainder, full-text articles were obtained for more detailed review. Articles were subsequently retained for data compilation or excluded according to several criteria. Studies needed to (i) be conducted on polyphenol-containing foods consumed by humans; (ii) investigate the effects of one or more processing methods on the polyphenol content of those foods; (iii) use extraction and analysis techniques that reliably quantified food polyphenols; (iv) give polyphenol contents, calculated by corresponding analytical methods, for the same foods before and after processing; (v) be published in English.
In addition, some articles were excluded owing to a lack of clarity or essential information. Data were extracted from retained publications by researchers with expertise in food technology, nutrition or agriculture. Data from each publication were entered in a standardized sequence to ensure consistency. As with previous releases of Phenol-Explorer, data were entered into different tables of a Microsoft Access database. The first release of Phenol-Explorer stored data in five tables (4). Four of these were independent tables named Foods, Publications, Compounds and Methods, whose records were each given unique identifiers. A fifth table, Food Composition, listed each concentration value collected as a record, accompanied by codes linking to the food, publication, polyphenol quantified and method of analysis.
To build the food processing module, data were entered into existing database tables as well as a further four tables that were added to the relational design: Retention Factors, Published Yield Factors, Yield Factors and Processes (Figure  1 ). The tables relevant to the retention factor module are described as follows: Food Composition: Polyphenol contents of raw and processed foods were entered into this table and converted to mg/100 g or mg/100 ml in the same manner as for dedicated composition data. Data used for the new food processing update were not added to the part of the database dedicated to the polyphenol contents of foods. Retention Factors: Identifiers for polyphenol contents of foods before and after processing were entered into this table along with an allocated yield factor for a given food and process.
Technical implementation of the update The new Microsoft Access tables were exported to the MySQL database that feeds the web interface. For Phenol-Explorer 3. 0, a faster, cleaner and more professional web interface has been implemented using the Bootstrap front-end framework (http://twitter. github. io/bootstrap/). This framework has also added support for mobile devices through the responsive design. The Ruby on Rails application framework (http://www. rubyonrails. org) has been upgraded for greater security and reliability and the javascript layer improved to allow faster page loading. The site is now integrated with MolDB, the same structure server used for the Human Metabolome database (http://www. hmdb. ca) (17) , which allows the efficient calculation of structural properties (such as molecular weight, formula and physicochemical properties).
Querying and output Retention factor data are searched for and retrieved in the same way as food composition data. A new button, food processing, is available in the site's main toolbar, from which either all foods or all polyphenols with retention factors can be browsed. If foods are selected, a list of all food and process combinations, the yield factor used, number of polyphenols for which retention factors are available and volume of data is displayed. This list can be browsed and filtered by any field, as with metabolism and composition data (Figure  2 ). For each food and process combination, a summary of retention factors is available by clicking on the corresponding retention factors button. A new page then displays the mean aggregated retention factors for each polyphenol or polyphenol group, along with standard deviation, range and links to references.
This display now contains the link show retention factors, which can be followed to retrieve aggregated retention factors. As with Phenol-Explorer composition and metabolism data, text boxes at the top of each column allow for further filtering of data. For Phenol-Explorer 3. 0, significant improvements to the overall user interface have been made. Pages for each of the three parts of the database (composition, metabolism and processing) have been more clearly separated and marked. The polyphenol and food classification hierarchy is displayed via a tree-like structure where the user can see previews of foods and polyphenols. The advanced search has been redesigned and the reports interface has been updated and improved.