dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion For version 3. 0 of the Phenol-Explorer database, we have added a new module on changes in the polyphenol contents of foods due to cooking and other processing methods. To our knowledge, it is the first database to collate this type of data for polyphenols. The USDA  factors for some carotenoids, but not polyphenols. Phenol-Explorer is dedicated to this highly consumed group of bioactive phytochemicals. In addition, data are presented through a user-friendly web interface that allows rapid searches to be performed and the origins of data to be easily viewed. In most cases, polyphenol contents of foods before and after processing were determined by high-performance liquid chromatography (HPLC) where identities could be verified with reference to authentic standards, and concentrations could be reliably calculated from chromatographic peak areas.
Therefore, retention factor values previously compiled for other nutrients were always <1 (in the USDA database for example, 100% retention or a value of 1 was assumed to be a maximum and any values in excess of this were rounded down to this figure). Although most polyphenols also degrade in this manner, retention factors >1, or an increase in the concentration of a particular polyphenol, are possible and occur in Phenol-Explorer 3. 0. For example, aglycone concentrations may rise where processing causes polyphenol conjugates to lose their sugar moieties or where phenolic acids are hydrolyzed from their polyol esters. For example, a retention factor of 2. 39 was calculated for quercetin aglycone in cauliflower following blanching  (14). Where polyphenols are hydrolyzed before analysis, only a reduction in aglycone content is theoretically possible.
In practice, the application of an unsuitable yield factor or poor analytical reproducibility could also explain an increase in polyphenol concentration. Care should therefore be taken when using retention factors greatly in excess of 1 for estimation of polyphenol intake. Phenol-Explorer 1. 0 was initially conceived to easily retrieve the polyphenol content of a food of interest. Although these estimations of polyphenol content were more advanced than what was previously available, they did not take into account the effects of cooking and storage on polyphenol-rich foods. In some cases, there were sufficient composition data on a particular processed food for a database entry separate from the raw food, such as orange juice produced from oranges. These processed foods were then fully documented in Phenol-Explorer 1.
0. However, a paucity of data on polyphenol content for many other processed foods meant that they could not be included in the first release of the database. Release 3. 0 allows the retrieval of the most suitable retention factor to be applied to more poorly documented processed foods. For those polyphenols for which more data were available, aggregated retention factors could produce more accurate values, although individual published retention factors are viewable if needed. One strength, therefore, of these food processing data is their variety and transparency and the resulting potential for imputation to missing foods, processes or polyphenols. The data collated for Phenol-Explorer 3. 0 allow the most detailed analysis so far of how different domestic and industrial processes affect the polyphenol contents of foods.
The volume of data collected on different foods and processes corresponded with their importance in terms of polyphenol dietary exposure. For example, the fruit and vegetable food groups and their main polyphenols accounted for the majority of the data, and the flavonols and hydroxycinnamic acids were the most studied polyphenol subclasses (Figure  4 ). Domestic processes such as boiling, steaming, refrigeration and storage at room temperature were most commonly applied. The database also helps identify those areas where more study should be undertaken. Data are most notably lacking for the cereal and oil food groups, which may contribute a substantial proportion of polyphenols to some diets. Similarly, few data were available for canning and drying processes, which are applied industrially to many widely consumed polyphenol- rich foods.
These aspects will be more extensively discussed in a separate article currently in preparation. Phenol-Explorer is the first web database to include retention factors on this important class of dietary bioactives. As with previous releases, all data are traceable back to the original publications, and data will be periodically reviewed and new data added where possible, taking into account user comments. The database should be invaluable for the refinement of calculation of polyphenol content in processed foods and of dietary exposure to polyphenols in epidemiological studies. It will allow estimation of polyphenol intake to a greater degree of accuracy than ever before from dietary surveys, with a view to the long-term goal of clarifying the links between polyphenol intake and major chronic disease.
ß The Author(s) 2013. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/ licenses/by/3. 0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. page number not for citation purposes) Database, Vol. 2013, Article ID bat070, doi:10. 1093/database/bat070.
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This led us to build a 3D model that included the hydrophilic NS2B cofactor region, a glycine flexible link, and the NS3 proteolytic domain in complex with the NDL inhibitor. To obtain this DENV NS2B CF -Gly-NS3 PRO model with as much structural information as possible, we performed an alignment among all Dengue virus and West Nile virus protease 3D structures available in the protein database (PDB) at the time (Table  1 ). This allowed us to check the completeness of the PDB structures compared to the sequences deposited by the same authors. From this analysis, we were able to gather information to build three different models of our system: Two preliminary models based either solely on the WNV or solely on the DENV PDB structures and a third one mixing data from both DENV and WNV. For each model, we prepared the PDB templates by removing unused chains, ligands, water and ions.
The cleaned structures were aligned using the Discovery Studio Visualizer 3. 1 (Accelrys, Inc) based on the sequence alignment of their conserved residues. Prior to submission to the homology software MODELLER  [42] , the sequence alignment obtained previously was edited to ensure certain features would be found in the resulting model, i. e. , the N-terminus region (including NS2B CF ) from WNV structures and C-terminus from DENV structures in the final model. For each set of templates (WNV only, DENV only or WNV/DENV), five homology models were generated, each with three loop refinements, giving us 45 models. The three lowest energy models (based on each set of templates) were chosen and verified by PROCHECK  [43]  to ensure their structural quality. We next visually compared these models and retained the one with features from both WNV and DENV structures, as it contained less disordered coils and loops.
Finally, we transferred the atomic coordinates of the NS2B cofactor and those of the NDL inhibitor (Bz-Nle-Lys-Arg-Arg-H) from the 2FP7 PDB template into the final NS3 PRO model as obtained above. After arranging the cofactor in place, we built the glycine linker with the homology module of the INSIGHT II package (Accelrys Inc). The inhibitor was deliberately left covalently unbound to simulate conformational changes in the active site prior the formation of the protease/substrate intermediate. Using the AutoPSF Generation Plugin provided with VMD  [44] , missing hydrogens were added according to their predicted protonation state at pH 7. 0 and histidine residues were assigned to their δ-protonated state.
Molecular Dynamics Simulations The final model of the complex between the NDL ligand and the NS2B CF -Gly-NS3 PRO target obtained above was submitted to MD simulations. The complex was firstly solvated with TIP3P Missing parameters for the ligand were added using the CHARMM22 force field  [45]. The NAMD software  [46]  was used to simulate the dynamic behavior of the complex using the parm. prm parameter file from CHARMM22 in periodic boundary conditions. Long-range electrostatics was evaluated using the particle-mesh Ewald approach  [47]. Simulations were carried out in the NPT ensemble using Langevin dynamics and piston to fix temperatures (300 K) and pressure (1 atm). Hydrogen-heavy atom bonds were constrained to their equilibrium values with the SHAKE algorithm  [48]. The system was first energy minimized (6400 conjugate gradient steps) and then equilibrated (500 ps) before recording the trajectories.
Clustering Once the MD simulations were complete, all frames were aligned by taking into account only heavy atoms in the core region of the NS3 protease alone (residue range from 20 to 168). This was done in order to focus the protein conformational analysis on the binding site region only. The RMSDs were calculated by using the appropriate VMD plugin and the corresponding data were exported as an ASCII matrix by the VMD Multiplot module. For clustering, a previously developed in-house Tcl script was used with a cutoff of 4. 5 Å. This script builds a 2D RMSD matrix for identifying conformational families within a given RMSD range.
Analysis software All molecular representations as well as several other analyses (such as distances between catalytic residues through the trajectory and secondary structure maintenance over time, for example) were performed by VMD. All graphics (RMSD, pair interactions, mobility plots, pocket volume evolution, etc) were plotted using GraphPad Prism 5 (GraphPad Software, Inc). Two-dimensional binding plots from docking results were generated with Discovery Studio Visualizer 3. 1 (Accelrys, Inc).
After being verified by PROCHECK, the complete NS2B CF -Gly-NS3 PRO model was found to be of acceptable accuracy (over 96% residues in allowed regions) and was chosen as definitive to carry on further studies (File S1). Figure  3B  shows the structural alignment of the finished homology model with the newly available ligand-bound DENV structure (PDB id: 3U1I)  [36]. The alignment has a C α atom RMSD of 0. 9 Å for 195 atoms, which gives consistency to our built model. Additionally, as pointed by Knehans et al. [24] , the oxyanion hole was also present in our model (Figure  3B , boxed detail), even though we did not used restraints during homology modeling. Those features, together with our model completeness, including both inhibitor and cofactor in the active conformations, as well as the gap-filling loop grafts directed by  ).
[24]. However, most of the other studies were based either on the retracted apoenzyme structure  [26, 59] , or in the open holoenzyme conformation  [23, 25, 58]  and were based on a rigid protein system with a single conformation. The first homology model for the DENV NS2B/NS3 protease was done by Brinkworth et al. in 1999  [61]. However, as it was built based on the more distantly related hepatitis C virus NS3/ NS4A protease (PDB id 1JXP  [62] ), several structural differences (Cα RMSD of 11 Å) were observed when comparing it to the recently resolved DENV-3 NS2B/NS3 protease (PDB id 3U1I  [35] ). Also, this work did not take into account the ligand-binding induced fit mechanism, as it was yet to be proposed  [33, 63] , and thus, ligand was absent from their model. Later, in 2007, Kee et al.
[64]  used this same model for automatic docking studies. In 2004 and 2005, respectively, Niyomrattanakit et al. [65]  and Chanprapaph et al. [60]  built homology models based on the now retracted apo DENV NS3 PRO structure. Similarly,  Ganesh et al. , in 2005 [59] , and  Tomlinson et al. , in 2009 [26] , used homology models based on the retracted structure for automatic docking studies. For studying the importance of domain motion between the NS3 PRO and NS3 HEL domains through MD simulations (1 ns), Rosales-León et al. , in 2007  [66] , built a full DENV NS3 model, but also based on this retracted structure. In 2010, two groups  [29, 67]  independently published a similar approach for building active conformational models, where the cofactor and the ligand were extracted from the WNV NS2B CF /NS3 PRO structure (PDB id 2FP7) and fused to the DENV NS3 PRO.
Acknowledgements We would like to thank  Dr. Tatsuya Nagata  for providing the sequence and the vector used for modeling and cloning the DENV 1  NS2B-NS3  protease,  Dr. Dave Ritchie  for the critical reading of the manuscript, and the  Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA) at Nancy, France , for providing computational resources.
The full list of putative prophage regions in the assembly is given in Additional file 2: Table  S3. Many horizontal gene transfer events into Wolbachia are likely to be mediated by bacteriophage, which are known to transfer laterally between Wolbachia strains coinfecting the same host  [37]  and are capable of transferring flanking non-phage genes in the process  [38] , thus facilitating horizontal gene transfer and genome diversification. To identify possible recent horizontal gene transfers into the wBol1-b genome, we used wBol1-b genes that had not been clustered with any other gene in the orthoMCL analysis (and were therefore putatively wBol1-b-specific) as blastp queries against the NR database. 26 of these genes had blastp matches to genes from other Wolbachia strains not included in the clustering analysis, and are thus components of the accessory genome but are not wBol1-b-specific.
A total of 44 genes are present in wBol1-b but no other currently sequenced Wolbachia strain (Additional file 2: Table  S4 ). Of these, 35 had no NR matches; these may be very rapidly evolving genes, genes in the late stages of degeneration, or the result of horizontal transfer from a genome not yet represented in the database  [39]. It is also possible that some, especially the shorter of these genes, could be artefacts of the annotation process. Finally, nine wBol1b-specific genes lacked Wolbachia homologs but had high-quality matches to non-Wolbachia genes in the NR database. All but one of these genes are either within or adjacent to phage regions. We searched for degenerate or unannotated copies of these genes in the wPip genome and found no evidence of them, and it is likely that they represent recent phage-mediated horizontal gene transfers into the wBol1-b genome that occurred subsequent to divergence from wPip.
Both genes have as their top NR blastp match the Solenopsis invicta (fire ant) gene SINV_00084. The 5' and 3' ends of SINV_00084 are similar to fragments of insect Golgi SNAP receptor complex genes, while the highly internally repetitive central portion of the gene matches a region of the Rickettsia massiliae dnaE2 gene, RMA_0751, which is part of the tra cluster region  [42]. This gene cluster, which encodes proteins involved in conjugal DNA transfer, is thought to have been laterally transferred into Rickettsia from Protochlamydia amoebophila, an obligate symbiont of amoebae  [43]. Given the highly repetitive nature of the sequences involved, and the few taxa for which matching sequences are available in Genbank, it is not currently possible to determine the evolutionary history of the possible transfers of this genetic region between wBol1-b, R.
massiliae, S. invicta or some number of possible intermediates not yet represented in the database. Finally, two additional wBol1-b genes, wBol1_1091 and wBol1_1092, discussed in further detail below, may have originated in eukaryotes. What are the proximate sources of these horizontally transferred genes? There is clearly no single taxonomic group represented in the NR database that shares close homologs of this complete set of genes with wBol1-b. Furthermore, for several of these genes, the closest known homologs are moderately divergent from the copies in wBol1-b, and it is unlikely that the genetic transfer took place directly between these taxa and Wolbachia. These genes are found associated with different phage regions of the wBol1-b genome, and may be the result of multiple independent transfers since divergence from wPip.
Both have full-length matches in the NR database only to insect proteins. wBol1_1091 has multiple full-length matches to hypothetical proteins in Culex quinquefasciatus and Aedes aegypti. The 5' half of the gene has matches only to the mosquito genes, but the 3' half of the gene, which contains the secA domain, also has further hits to basal eukaryotes, including Polysphondylium pallidum, Tetrahymena thermophila and Salpingoeca sp. There are no close bacterial matches. The highest-ranked bacterial match has low-level and fragmentary sequence similarity with a portion of the secA domain region of wBol1_1091, and has a bit score of only 108, compared to scores of 200 to 957 for the eukaryotic matches. wBol1_1092 has full-length hits to multiple secA proteins in the jumping ant Harpegnathos saltator, and a near-full length hit to a hypothetical protein in Daphnia pulex.
The region of the protein containing the secA domain has hits to hypothetical proteins in other insects including Drosophila willistoni, Culex quinquefasciatus and Tribolium castaneum. This region also has less significant hits to proteins from basal eukaryotes including Tetrahymena and Polysphondylium, and more distantly to bacterial secA proteins. In the wBol1-b draft genome assembly, wBol1_1091 and wBol1_1092 are surrounded by known Wolbachia genes (Figure  3 ). We confirmed the position of these eukaryotic-like secA genes in the Wolbachia genome, and their absence from uninfected host material, using PCRs spanning wBol1_1089-wBol1_1091 and wBol1_1092-wBol1_1093, on infected and antibiotic-treated lines of H. bolina. Other Wolbachia homologs of these secA genes were not found in complete genomes or in the NR database.
However, matches were found to whole genome shotgun sequence data from the NC48S strain of Drosophila simulans, sequenced as part of the D. simulans genome project  [45] , when blasting against the NCBI WGS database. The NC48S line was collected in Noumea in 1991, and is superinfected with two Wolbachia strains, A group wHa and B group wNo  [46]. None of the other six lines of D. simulans sequenced for the D. simulans genome project, whether Wolbachia-uninfected or infected with wRi, appeared to contain sequence matches to these secA genes. To determine which of wHa and wNo carries the secA homologs, we used NC48S lines in which the superinfection has been separated out into single infections. We amplified and sequenced portions of the homologs of both wBol1_1091 (516 nt) and wBol1_1092 (3018 nt) from the line infected with wHa, but could not amplify these regions in either the line infected with wNo or the antibiotic-treated uninfected lines.
) Figure  4  Maximum likelihood phylogenetic tree based on the amino acid sequences of Wolbachia secA gene wBol1_1092 and its homologs. Prokaryote and eukaryote secA genes cluster into two separate, well-supported clades. There are two apparent transfers from eukaryote to prokaryote species, marked with arrowheads. Bootstrap values over 50 are shown; for clarity they have been removed from short internal branches within the prokaryote clade and the large Daphnia and Harpegnathos clades. Each tip is labelled with the sequence's Genbank GI number and the species name. repress male killing  [56] [57] [58] , and this should be taken into account in future investigations of the mechanism underlying this phenotype.
wBol1-b genome sequencing and assembly Approximately 5 μg of Wolbachia DNA was provided to AGRF (Australian Genome Research Facility), which generated a 454 GS-FLX shotgun library. An additional sample was then provided to AGRF to generate a 454 GS-FLX paired-ends library. Libraries were combined for genome assembly. Genome assembly was done by AGRF following the protocol provided by the platform supplier (Roche) and based on a shotgun-data-first addition order. Contaminating mosquito sequence was identified by using contigs as blastN queries against the NR database and identifying high-similarity matches. The assembly was manually edited using Artemis  [63]. The final assembly consists of 91 contigs, ranging from 644 bp to 155817 bp in length, arranged into 13 scaffolds, and an additional 53 unscaffolded contigs with lengths less than 2 kb.
The sequence data has been deposited at the European Nucleotide Archive (CAOH01000001-CAOH01000144) in BioProject PRJEB566. We confirmed that the strain sequenced was a clonal lineage by mapping the reads to the assembly using Newbler v2. 6 (Roche) and checking for evidence of wellsupported SNPs, which would suggest that a mixed culture had been sequenced. All high-confidence SNPs were associated with known imperfect repeats (data not shown), indicating that the strain sequenced is highly likely to have been clonal.
Annotation Initial annotation of the wBol1-b draft genome assembly was performed using SUGAR (Simple Unfinished Genome Annotation Resource), an annotation pipeline consisting of several Perl scripts, controlled by a user defined instruction file (Szubert & Beatson, In Prep. ). The program makes use of the NUCmer component of the MUMmer 3. 0 package  [64]  for ordering an unfinished genome against at least one reference sequence. Glimmer 3. 02  [65]  was used for protein coding gene calling (after punctuating contig boundaries with a six frame stop-start sequence), based on a set of observed long ORFs, with optional scanning for genes matching over boundaries, and improvements to paired ends derived scaffolding. Automated annotation of proteins was based on a diminishing identity threshold scale for Blastp  [66]  matches against protein databases consisting of (1) the reference genome wPip, (2) other Wolbachia genomes, (3) swiss-prot and (4) the non-redundant database (NR).
Ortholog prediction We performed an all-versus-all blastp analysis of the predicted proteomes of wMel, wRi, wPip, wBol1-b and wBm, then used orthoMCL  [27]  to group orthologs and their recent paralogs (lineage-specific duplications) into 'ortholog clusters'. We ran the analysis multiple times using E-value cut-offs of 1e-05 and 1e-10, and with inflation values of 1. 2, 1. 5, 2. 5 and 5. The great majority of core ortholog clusters were identical across runs; we performed all further analyses on results of the run with default settings (E-value 1e-5, inflation 1. 5). We used purpose-written Perl scripts to parse the results of this analysis and identify clusters of core genes. To confirm that the ortholog clustering was reasonable, we compared the core gene ortholog sets produced by orthoMCL to those predicted using two other methods: a simple all-versus-all mutual best blast hit analysis, and the sets of single-copy positional homologs inferred by Mauve  [71]  after alignment of the five genome sequences.
wBol1-b-specific genes To identify genes specific to wBol1-b, we used the amino acid sequences of all of the wBol1-b genes that were not clustered with any other genes in the orthoMCL analysis as blastp queries against the NR database with a very low stringency E-value threshold of 10. Genes were considered putatively wBol1-b-specific if they had either no hits to the NR database with this cut-off, or had no hit to any Wolbachia gene with a better E-value than the best hit to a non-Wolbachia gene.