dataesr/scientific-paragraphs-categorization huggingface dataset:
Results In this study, the default MRS data processing software, run on a Siemens Trio Tim 3. 0T MRI's workstation, was selected as a reference approach and was called "Siemens" in the present study. "Siemens" was used to evaluate the performance of the proposed AQoCE method. Both AQoCE and "Siemens" were used to analyze all 78 cases, including 36 cases of LGG and 42 cases of HGG: 18 astrocytoma (WHO II), 5 oligdendroglioma (WHO II), 12 oligdendro-astrocytoma (WHO II), 1 mixed neuro-glioma (WHO II), 10 anaplastic oligdendro-astrocytoma (WHO III), 7 astrocytoma (WHO III), 24 glioblastoma (WHO IV), 1 gliomatosis cerebri (WHO IV). The results are as follows. First, 1 H-MRS is valuable in the clinical diagnosis and prognosis (by identifying the glioma grade). Gliomas consist of abnormal glial cells proliferation.
It would be of value to analyze their characteristics and evaluate their diagnostic value based on obtained metabolic data in order to improve the diagnostic and prognosis accuracy for gliomas. Finally, apply the new approach on combinations with other new magnetic resonance imaging technologies such as PWI, for higher specificity and sensitivity in glioma screening  [38] [39]. An automated CSI data processing software that implements AQoCE is shown in S1-S3 Figs. The user only needs to load CSI raw data acquired by the Siemens MR system (with the extension of. rda) and click on any voxel of interest or select any number of voxel of interest, then all of the selected multi-voxel MRS results, both wave form and peak value will be immediately displayed in a new window.
Conclusions In this work, we propose a new automated quantitative approach based on a convex envelope (AQoCE) and apply it to multi-voxel 1 H-MRS analysis. Under the limited clinical data used, this approach can efficiently evaluate brain glioma grading and demonstrate characteristics of brain glioma metabolism. It can also suggest the presence of peritumoral area infiltration. AQoCE has been integrated into our MRS data processing software and will be available as a free download on the following website: http://mdpslab. voog. com/softwares.
prausnitzii was the major contributor to the production of valine and leucine in the FBBR community. Furthermore, the experimental data showed that substitution of E. rectale with F. prausnitzii decreased the level of butyrate in the media, due to the higher capacity of E. rectale for butyrate production  (Louis et al. , 2010) , and also the model simulations showed a slightly lower butyrate production by the FBBR community compared with the EBBR community. Next we calculated the centrality scores for each bacterial species in order to identify which species have a dominant role in the overall metabolic conversion in each community (Extended Experimental Procedures). We observed that E. rectale and B. thetaiotaomicron were the main receptor and effector, respectively and thus represent key species (Figure  3A ).
Experimental procedures Reconstruction/updating of GEMs The B. adolescentis, B. thetaiotaomicron, E. rectale and F. Prausnitzii metabolic models were already published  (El-Semman et al. , 2014; Shoaie et al. , 2013). These models were validated individually and updated based on extensive bibliometric survey of the literature and databases  (Henry et al. , 2010). The R. bromii and L. reuteri metabolic networks were reconstructed based on automatic and manual curation, considering information available in the literature and databases. For model reconstructions, updating and quality checks, the RAVEN toolbox was used  (Agren et al. , 2013). The defined metabolic task file was implemented to perform the gap filling process. This task file includes synthesis of amino acids, nucleotides and carbohydrates.
Intensity (or expression) of 15 metabolites of interest was extracted for HGC and LGC subjects at two time points. Two sample t-tests were performed for detecting the significant metabolites and P value < 0. 05 was considered to be significant. All data are shown as mean ± 1 SD. The results were compared with data from triplicate in-vitro experiments for EBBR and FBBR communities. In CASINO the interactions of the bacteria as well as the phenotype of the community were identified using an optimization algorithm. Growth of each bacterium had local optimum whereas the community had global optimum. The community optimum was detected by the intersection point of the fixed constraints for the community and the calculated dynamic constraints, which was obtained by summation of the local and community forces.
Acknowledgements We thank  Dr. Rosie Perkins  ( University of Gothenburg ) for editing the manuscript,  Dr Ana Luisa Neves  for NMR sample preparation and  Suwanee Jansa-Ard  for metabolomics sample preparation. This project was supported by  Knut and Alice Wallenberg Foundation , the  Bill & Melinda Gates Foundation ,  Torsten Söderbergs Stiftelse ,  European Commission   FP7  project  METACARDIS  with the grant agreement  HEALTH-F4-2012-305312 ,  Fondation Coeur et Artères and French National Agency of Research (ANR-MicroObese , and " Investissements d'avenir "  ANR-10-IAHU-05 ).
Second, the cellular process(es) in which the clusters are involved were identified based on the BP Gene Ontology (GO)  14  annotations of their constituent proteins: GO terms annotating at least 50% of a cluster's proteins are assigned to the cluster, which can now be called a 'functional module'. Each individual protein then inherits the annotations of its module(s) in addition to its own. This annotation procedure favours the detection of functionally homogeneous clusterslikelier to represent functional modules-and maximize the number of clusters successfully annotated. Finally, to distinguish extreme from 'classical' multifunctional proteins, proteins found at the intersection of functional modules annotated to dissimilar functions were identified. Function (dis)similarity is given by two metrics of GO term association (made available in the PrOnto database; see Methods and (http://tagc.
univ-mrs. fr/pronto)) based on the frequency of co-occurrence of a GO term pair either in a protein's or in a pair of interacting partner's annotations. Using these metrics ensures that the multiple functions in which the candidate protein is found to be involved are very rarely performed (i) by a single protein and (ii) by interacting proteins, two proxies that we consider indicators of unrelated functions. Our pipeline (MoonGO) is explained in more detail in the Methods section and is summarized in Fig. 1. A set of 430 EMF candidates. We have applied our pipeline to a large, high-quality human interactome (74,388 interactions between 12,865 nodes, Supplementary Data 1) built by extracting data from online databases (see Methods). The 855 overlapping clusters returned by OCG contained 33.
4 proteins on average. Of these, one or several BPs were assigned to 633 (74%), based on the annotation of their constituent proteins (Supplementary Data 2). All network proteins belonged to at least one annotated cluster. As expected from a previous analysis, around a third of the interactome (3,846 proteins, 29%) belonged to several clusters and can therefore be considered multifunctional  13. Of these, 430 proteins (10%) are found at the intersection of clusters annotated to dissimilar functions and are considered EMF candidates (Supplementary Data 3). As candidates are defined with respect to their module's annotations, their identification is dependent on the quality of these annotations. Said quality was assessed by performing three types of randomization tests. First, the annotations of all proteins were shuffled, and candidates were identified using these randomized annotations.
As MPs are a subset of EMFs, we compiled a list of 39 known human MPs from the literature and checked whether they were found as candidates. Six of the thirty-nine belonged to dissimilar modules and were found. Although finding 6 out of 39 when identifying 430 candidates out of 12,865 represents an enrichment of 4. 6-fold compared with expected, with a significant P-value (1. 4e À 3 , hypergeometric), 6 of the remaining 33 could not be found by MoonGO since they belonged to clusters that could not be annotated. We cannot thus exclude that the remaining 27 proteins were missed because of ill-annotated clusters or because all their interactions have not been discovered yet. Overall, these different assessments confirmed the specificity of our approach and consolidated our confidence in the identified candidates.
6) versus Hubs (55. 5)) not all candidates have a high degree (see Fig. 2a ) and only 20% of network hubs were found as candidates, demonstrating that a high degree is neither necessary nor sufficient for consideration as a candidate. Candidate sequence and structural features. Protein interaction and functional annotation databases tend to refer to genes rather than gene products and do not differentiate between different protein isoforms  15. It is therefore possible that the multiple functions of our candidates are actually carried out by different isoforms of the same gene. However, candidates do not have significantly more isoforms than the Multi-NCs (mean Cands (2. 2) versus Multi-NCs (2. 1), Wilcoxon test P-value ¼ 0. 2; Fig. 2b ), suggesting that the number of isoforms is not a defining characteristic.
6 ). Interestingly, although candidates are not distinguishable from either Multi-NCs or the network average, they are significantly less disordered than hubs (mean Cands (37. 3) versus Hubs (40. 5), Wilcoxon test P-value ¼ 0. 028, see Fig. 2d ). This trend was confirmed by the results of ten other disorder predictors whose results were retrieved from the D2P2 database  17  (see Supplementary Figs. 7 8 9 10 11 12 13 14 15 16 ). These results suggest that, despite their high average degree, EMFs, unlike regular hubs, are under stronger selective pressure to maintain a stable secondary structure. Eukaryotic linear motifs (ELMs) are short stretches of amino acids often located within intrinsically disordered regions, which have been shown to help the targeting of proteins to specific subcellular localizations, determining the modification state of proteins, or regulating protein activity in a context-dependent manner  18.
With respect to hubs, the same trend was observed, although not statistically significant. It therefore appears that candidates differ from hubs in their disorder content and from Multi-NCs in the number of linear motifs per residue. Finally, the different groups were also checked for the presence of proteins identified as containing ELMs involved in functional switches, the status of which ultimately affects the function of the ELM-containing protein (collected in the switchELM database  19  ). Candidates show a 6. 2-fold enrichment in such proteins (hypergeometric P-value ¼ 1. 2e-27), compared with a 1. 8-fold among Multi-NCs (P-value ¼ 2. 44e-14), 4. 68-fold for hubs (P-value ¼ 1. 07e-62) and a depletion in Mono (2. 7-fold less, P-value ¼ 2e-49). When different types of functional switches are considered, the candidates are enriched in proteins containing binary switch motifs (with an ON/OFF state) modulated by allosteric effects (1.
It is found at the intersection of a cluster annotated to 'RNA splicing, via transesterification reactions with bulged adenosine as nucleophile' (BP, GO:0000377) and 'nucleoplasm' (CC), which would correspond to its known function, and another annotated to 'response to endogenous stimulus' (BP, GO:0009719) and 'cytosol','nucleoplasm part' and 'intracellular non-membrane-bounded organelle' (CC), suggesting a novel role for this protein. Although no involvement in a signalling pathway has been shown to date, the protein contains two WW domains able to interact with proline-or phosphoserine-phosphothreonine-containing motifs and known to mediate regulatory interactions in various signalling pathways such as Hippo  26. MoonDB. We have collected our results in MoonDB, a database that includes the 39 human MPs used here as well as the full list of EMF candidates.
Methods Networks. Interaction data were retrieved using the PSIQUIC  38  interfaces of the APID  39  , BioGrid 40 , IntAct 41 , DIP  42  , MINT  43  , MatrixDB  44  , Reactome  45  , InnateDB  46  , MolCon, Spike  47  and TopFind 48 databases. They were filtered according to their identification methods and only binary interactions between proteins were kept. Protein names were mapped to UniProt IDs, and sequences clustered using CD-HIT  49. TrEMBL/SwissProt protein pairs sharing Z95% similarity were considered to be the same protein: interactions of the TrEMBL protein were then inherited by the Swiss-Prot protein. Self interactions were discarded. The final result was high-quality interactomes consisting entirely of experimentally verified, direct, binary interaction pairs. The CCSB network was downloaded from the CCSB Human Interactome database  22.
For the interaction probabilities, N is the number of interactions in our network between proteins with at least two different annotations, K is the number of interactions involving proteins annotated to GO 1 , n the number of interactions involving proteins annotated to GO 2 and k the the number of interactions between a protein annotated to GO 1 and one annotated to GO 2. To be considered dissimilar by our pipeline, two GO terms must have a probability of r0. 05 for both metrics. We have made these probabilities available in the PrOnto database, which is freely available at http://tagc. univ-mrs. fr/pronto/. Note that although PrOnto offers cross-ontology probabilities, only the BP probabilities were used in the present work to identify candidates. A more detailed explanation of our choice of developing PrOnto rather than using existing semantic similarity measures and a comparision of PrOnto and Semantic Similarity measures are provided as a Supplementary Note 1.
The betweeness and shortest path analyses were done using the R igraph library  51. Domain predictions were made using pfam_scan. pl  52 , the results shown in Fig. 2c  include both PfamA and PfamB domain predictions. Protein disorder values were obtained using disopred  16  with default settings. Figure  2d  was generated by plotting the sum of disordered residues of each protein. Protein isoform information and OMIM annotations were downloaded from UniProt and protein annotations from the EBI's QuickGO server (https://www. ebi. ac. uk/ QuickGO/). Expression data were taken from ref. 53. Protein phosphorylation predictions were made using GPS 2. 1 (ref. 54). Conservation. The protein sequences of all network proteins and their annotated homologues in yeast, mouse, fly and worm were aligned against each other using t_coffee  55.
The homologous sequences were taken from EnsEMBL  56. To obtain an indicative conservation value, the bit score of the alignments was divided against the length of the human homologue and the resulting values were compared. ELMs. The coordinates of all annotated ELMs in our network's protein were obtained from the ELM database  57. These were mapped to the disordered regions predicted by DISOPRED. Only ELMs that fell entirely within or which overlapped perfectly with a disordered region were counted. ELMs that overlapped but extended beyond the disordered region were ignored. To avoid misrepresentations caused by differing protein or disordered region lengths, the number of ELMs in disordered regions was divided by the total number of disordered residues of each protein. Similarly, the number of ELMs per protein was divided by the total number of residues of each protein.
Acknowledgements We thank  Carl Herrmann  and  Anaı ¨s Baudot  for helpful discussions and advice and to  Matt Oates  for his kind help with the D2P2 database. The work was funded by the  ANR (Agence Nationale pour la Recherche)  as a  PIRIbio grant   09-PIRI-0028 ,  Moonlight  project to C. B. and supported by an A* MIDEX  project ( ANR-11-IDEX-0001-02 ). C. E. C. is a postdoctoral fellow of the Fondation pour la  Recherche Me  ´dicale and B. R. is the recipient of a PhD fellowship from the  AXA Research Fund.
CONCLUSION In this paper we present an automatic ontology evolution tool called DYNAMO MAS. This tool is based on an adaptive multi-agent system. First, DYNAMO MAS evolved an ontology in OWL from text. Secondly, it reacts to ontologist feedback and produces new ontology evolution proposal. We showed in Section 3 the different agents behaviour against elementary and composite changes. We noticed that the adaptive skills we added to term and concept agents allow them to detect the uselessness of some proposals, to avoid the useless and wrong ones and to propose others. The originality of our work is that we exploit the ontologist feedback to improve the future propositions and reduce his frequent involvement. To validate the efficiency of DYNAMO MAS extension, we are currently working in three tasks: • Implementing the adaptive behaviour skills of our term and concept agents; • Designing and developing a new DYNAMO MAS graphical interface between ontologist and MAS to enhance the time of the interaction and make it shortened; • Testing the system with different ontologies in french and english languages.
In spite of this, the number of deposited entries in the Protein Data Bank is regularly increasing  (Kozma et al. , 2013). Small-angle X-ray scattering applied to biological macromolecules (BioSAXS) is a low-resolution structural technique that is particularly well adapted to monitor ternary and quaternary conformational changes of soluble proteins in their buffer solution  (Koch et al. , 2003). Although SAXS does not provide molecular-level resolution, it is particularly accurate in distinguishing between different structural models proposed from higher resolution techniques, and has become a popular technique among protein crystallographers  (Hura et al. , 2009). Applying SAXS to solubilized membrane proteins has long been hindered by the necessary presence of detergent molecules in solution, which leads to extra contributions to the measurable signal that are difficult to separate from the protein signal.
We also briefly describe the program Memprot that we have developed to systematize the SAXS calculations from the geometrical models and which is to be released to the community. In a subsequent development of our software, for cases in which the protein contour is less isometric than that of AQP-0, we consider developing a parameterized geometrical model of the detergent corona which adheres more closely to the actual shape of the protein. We expect the rules determined here to be sufficiently general to be applicable to these future modified coronas.
Modelling methodology The coarse-grained modelling based on a geometrical description of the detergent corona around a transmembrane protein has been thoroughly described in the work of  Berthaud et al. (2012) , while further insights into its validity to describe SAXS data were obtained by comparisons with ab initio and molecular-dynamics models  (Koutsioubas et al. , 2013). Here, we briefly review this geometrical approach and also describe the new features that are implemented in the computer program that accompanies this paper. The protein-detergent complex model is based on an allatom representation of the open-pore version of aquaporin-0 (PDB entry 2b6p, with added residues 1 and 36, from  Gonen et al. , 2005)  and on a coarse-grained network description of the detergent corona around the hydrophobic protein surface.
The different electron densities of the hydrophobic and hydrophilic regions of the corona are taken into account by placing two specifically chosen types of pseudo-atoms at the nodes of two densely packed cubic networks with different network spacing. Once the models have been generated and formatted as a PDB file, the calculations of SAXS profiles are performed using the program CRYSOL  (Svergun et al. , 1995). The pseudo-atoms were chosen within the CRYSOL look-up table, which contains for each atom its intrinsic excluded volume (V vdW ) and its number of electrons (n e À ). For a given electron density of the corona model, the spacing of each of the two networks of elementary cell volume (V elementary cell ) are related to the previous quantities according to the relation ¼ n e À À V vdW 0 V elementary cell þ 0 ; ð1Þ where 0 is the buffer electron density and is the electron density of the specific region.
Computer program The procedure for the fitting of SAXS data from membrane protein-detergent systems based on a geometric representation of the detergent corona has been implemented in a computer program written in Fortran 90 called Memprot. In order to perform a fit the user is asked for the all-atom PDB structure file of the correctly oriented protein, for the experimental curve, if possible accompanied by the error estimates, and for the range of parameter space (a, b, t, e, ') that will be scanned. An initial guess about the electron density of the different parts of the corona is also needed. The program proceeds by thoroughly scanning the parameter space (with a selected step for each parameter) in an effort to recognize the model parameters with the lowest discrepancy against the experimental data (Fig.
2 ). In order to ensure accuracy in the final results and also the fastest possible run time, the user is asked to limit the number of spherical harmonics L used by CRYSOL according to the relation L = 5 + Q max D max /2 , where Q max is the maximum considered momentum transfer and D max is the maximum diameter of the protein-detergent complex that may be estimated from the experimental data using GNOM  (Svergun, 1992). The latter relation comes from a conservative estimation of the higher order Bessel function contributions as implemented in CRYSOL calculations. The detergent corona models are always generated around the z axis and centred at z = 0. For this reason, a small script was developed that helps the user to align the protein along the z axis and also to bring the middle of the transmembrane plane to z = 0, resulting in a new PDB file with the protein in a suitable orientation.
Furthermore, the user may also fine-tune the elevation of the protein with respect to the xy plane in cases in which the hydrophobic transmembrane surface is not easily identifiable. Overall execution times depend linearly on the number of steps required for each of the parameters that are scanned. At the end of each run the user is provided with ASCII files that summarize the results and also with detailed fitting curves and PDB files for each of the trial models. The program depends on the installation of CRYSOL. Executables for the program are available for all major platforms (Windows, Mac and Linux).
Overall fit sensitivity to the membrane structure As previously discussed, the methodology developed at this stage does not aim to resolve the low-resolution structure of membrane proteins of completely unknown structure. Rather, the modelling of the detergent corona may provide a route for (i) the validation of candidate structures of membrane proteins or (ii) the study of extra-membraneous conformational changes associated with the function of the protein. In this respect, it is of interest to quantify the effects of small structural modifications of the protein structure itself on the overall goodness of fit of the model. In order to do so, we a low-model of the detergent corona obtained for the full aquaporin-0 conformation (PDB entry 2b6p) and we calculated the associated scattering curve with a truncated aquaporin-0 structure with the extramembrane C-terminal domains missing (22 residues per monomer).