dataesr/scientific-paragraphs-categorization huggingface dataset:
Examples are those addressing technical functionalities, such as the Technical Feasibility Tool, the Model Discovery and Understanding, the Requirements Specification Tool, the Optimization Tool, the Target Generation Tool, the Deployment Tool or the different Testing Tools. • Sparx Enterprise Architect (EA)  [23] , that includes a set of add-ons developed which support the discovery and target generation of. NET C# based applications. The proposed suite supports the migration of both Java and. NET applications. However, Eclipse support for. NET development is somehow limited. NET applications can be developed using Microsoft Visual Studio or other corresponding development environments. Our approach for the migration of. NET application relies on the usage of Sparx EA, during certain tasks of the methodology (notably during the Model Discovery and the Target Code Generation tasks).
Thus, models of. NET applications will be obtained out of. NET projects using EA, and the final target code will be generated out of the migrated models using EA as well. The remainder of the methodology tasks involving modelling are supported by Eclipse ARTIST tooling. The migrated. NET code can be compiled and assembled for deployment using Microsoft Visual Studio or another compatible development environment. Similarly, Java-based applications can be developed using the Eclipse JDT or another Java development environment. The projects of these Java applications can be then easily imported into the Eclipse workspace, ready to be managed by the tools. EA and Eclipse ARTIST tooling exchange models (notably UML ones) using the XML Metadata Interchange (XMI)  [18]  serialization format. We foresee that the interactions between the browser-based and the Eclipse-based suites will be user driven and mediated through the Eclipse workspace and the repository.
In this section, the proof of concept for the Methodology Process Tool based on an experimental migration project is discussed. The proof of concept will show how specific steps of the generic methodology are customized for a concrete migration project based on the pre-migration analysis results. The Evaluation of the proposed methodology and its respective implementation, the Methodology Process Tool, was based in an experimental legacy J2EE Application, the PetStore  [19]. In the following sections we describe the steps for the business and technical analysis of the application during the pre-migrations phase and then, based on the analysis results, we describe the rule-based customization of the methodology through MPT. 6. 1. PetStore Business Case. Our objective is to tackle the problem of software modernization under two perspectives, the technical one and the business one.
This is the reason why we need to define the business case of the PetStore in order to evaluate and test all the tools under both perspectives: SME-Software is an English company producing and marketing software products targeting SME Enterprises. Their customers include Hotels, Restaurants, Stores, Chartered Accounting Firms, Employment Law specialists. PetStore application started as a management system or ERP for stores selling pets, and evolved as a web portal for selling pets over Internet. Nowadays SME-Software sells its application by licensing (to be renewed each year) and offers the possibility to customize it through consultancy services via ad-hoc projects. This customization includes the purchase of the new infrastructure (Application server, database server, etc. ), installation of the adequate application server With the emergence of cloud computing and the SaaS business model, SME-Software managers have start thinking about offering their Software as a Service.
The effort required to accomplish each migration task is derived from the complexity of the task and the complexity inherent to the legacy code (component complexity). TFT analysis results are summarized in the following table. In the example shown, the different components of the PetStore have been identified ("component" column) and the following metrics have been obtained for each of them: • Component complexity: This is the complexity related to the legacy software. In this proof of concept, just for the sake of simplicity, component complexity is just measured by the number of files comprising the component: 1 XML file for J2EE resources (complexity 1. 0) and 40 Java classes for PetStore (complexity 40. 0), 1 SQL file for PetStore data schema, 1 bundle file (installation) for J2EE Server and Non-SQL Server.
BFT results. In the business feasibility analysis the following business scenarios were identified:  The MPT is developed with a set of rules for the customization of the methodology based of the parameters of each migration scenario. In the proof of concept, we present the following example rules which were applied to the pre-migration analysis results of the PetStore application. These example rules are based on the technical and business migration goals defined by the user and extracted from MAT. Example Rule #1 (technical level): This rule examines a) the parameter databasereq which refers to the database requirements (requirements elicitated by the application owner for the database) and b) the parameter targetplat which refers to the (cloud) target platform selected by the application owner (where the application will be deployed).
6. 2. 1. MAT results. The Maturity Assessment Tool results on application positioning with respect to its cloud maturity are the following: Model the application to have a better knowledge of the application -Redesign the database to be multitenant -Redesign the architecture and distribution of stateful/stateless nodes -Integrate a module of concurrent users monitorization -Define authentication mechanisms -The coexistence of the two business models may bring difficulties in the support processes -Define new SLAs taking into account the infrastructure part 6. 2. 2. TFT results.
6. 2. 1. MAT results. The Maturity Assessment Tool results on application positioning with respect to its cloud maturity are the following: Model the application to have a better knowledge of the application -Redesign the database to be multitenant -Redesign the architecture and distribution of stateful/stateless nodes -Integrate a module of concurrent users monitorization -Define authentication mechanisms -The coexistence of the two business models may bring difficulties in the support processes -Define new SLAs taking into account the infrastructure part 6. 2. 2. TFT results. • Migration Goals -Migrated programming language: J2EE -Multitenancy level: Multitenant -Database scalability: High scalability of data -Interoperability requirements: Yes, ERP -SLA requirements: System availability, performance and QoS, Outages covered, Data integrity, business continuity -Configuration requirements: User personalization -Authorization requirements: ID, Password -Cloud provider: Amazon, Public Cloud -Elasticity: Component / self-made means to ensure its elasticity • High level recommendations -.
, 2006) , thermodynamic parameters (Gibbs free energy, enthalpy, entropy), kinetics (association and dissociation rates, ka and kd respectively) and affinity (K D ) reflecting the strength of the interactions. The goal of this work is to investigate the relationship(s) between affinity and kinetics of heparin-protein interactions and the localization of heparin-binding proteins, their intrinsic disorder, their molecular functions and the biological processes or pathways they are involved in. We have also compared the affinity of proteins for heparin, low molecular weight (LMW) heparin and heparan sulfate. We built for this purpose a dataset of protein-heparin interactions characterized by SPR because this technique allows the calculation of kinetic parameters and affinity. Furthermore the selection of a single technical approach avoids the discrepancies that might have been introduced by including interactions characterized by different techniques in the dataset.
Heparin-protein interaction dataset The dataset of protein-heparin interactions integrating affinity (K D ) and kinetics (ka, kd) was built using data from MatrixDB database (98 for HP, 40 for HS) (http://matrixdb. ibcp. fr/, Chautard et al. , 2011 Chautard et al. , , 2009;; Orchard et al. , 2012)  and from manual curation of the literature (www. ncbi. nlm. nih. gov/pubmed/). We also included experimental data from the laboratory on protein-heparin/heparan sulfate interactions such as endostatin, a fragment of collagen XVIII  (Ricard-Blum et al. , 2004), collagens I and V (Ricard-Blum et al. , 2006b) , α5β1 and αvβ3 integrins  (Faye et al. , 2009b)  and procollagen C-proteinase enhancer (PCPE-1,  Weiss et al. , 2010). We selected protein-heparin/ heparan sulfate interactions characterized by SPR, both to build a dataset comprising kinetic data and to reduce as much as possible the heterogeneity of the dataset.
We extracted the affinity (185 K D values) and kinetic parameters (130 values of association and dissociation rates, ka and kd respectively) from 69 publications reporting interactions of proteins with heparin, low molecular weight heparin and heparan sulfate. We included interaction data for low molecular weight heparin (Mw b 9 kDa) and heparan sulfate only when interaction data for heparin were available for the same protein (Table  1 ). The dataset comprised 160 K D values and 118 ka/kd values of protein-heparin interactions from 65 publications, 71 unique protein-heparin interactions, 49 unique fragment-heparin interactions and 5 lipoprotein particles-heparin interactions (Table  1 ). Heparin-binding sequences which have been experimentally determined were integrated in the dataset when available.
Do the affinities of proteins for heparin depend upon their categories? We first determined if different categories of proteins exhibited differences in affinity for heparin and/or in their binding kinetics to heparin. Individual values and the results of statistical analyses with p values b 0. 05 are provided in Supplementary Tables 2-6. The three nuclear proteins of the dataset bound to heparin with low affinity (mean K D value = 1. 15 × 10 -5 M) but this group was too small to be included in the statistical analysis. Matrisome-associated proteins bound to heparin with higher affinity than the other protein categories, except for lipoproteins, which had a significantly higher affinity for heparin than all the other categories (Fig. 1A , Supplementary Table  3 ). Matrisome-associated proteins and lipoproteins bound faster to heparin than the other proteins of the dataset, the differences being significant between matrisome-associated proteins and fragments (p b 0.
, 2000; Huang and Liu, 2009)  and intrinsically disordered proteins have been reported to interact with their partners with high specificity and low affinity  (Babu et al. , 2011). Proteins usually bind to heparin via sequences rich in basic amino acid residues such as lysine and arginine  (Capila and Linhardt, 2002) , which are disorderpromoting residues  (Uversky and Dunker, 2010). This prompted us to study the relationship of the global disorder of the proteins of our dataset and their affinity for heparin. No obvious correlation was found between the percentage of intrinsically disordered residues in proteins and their affinity for heparin (Fig. 2A ), their rate of association with heparin (Fig. 2B ) or their rate of dissociation from heparin (Fig. 2C ). We then predicted the amount of disordered residues in experimental binding sites and studied its relationship with affinity and kinetics.
Does the size of proteins and heparin matter? The comparison of the affinity and kinetic parameters of fulllength proteins and their fragments for heparin was performed on 15 and 12 pairs respectively. 87% of full-length proteins had a higher affinity for heparin than their fragments with the exception of the two fragments of α5β1 integrin (Fig. 3A ). Most proteins (67%) bound faster to heparin than their fragments (Fig. 3B ) and had a slower dissociation rate than their fragments (Fig. 3C ). Full-length proteins formed thus more stable complexes with heparin than their fragments. In addition 75% of proteins bound with a higher affinity to heparin than to low molecular weight heparin (n = 8, Supplementary Fig. 1 ). No kinetic data were available in the dataset for these interactions.
Do proteins interact differently with heparin and heparan sulfate? Not all the interactions characterized with heparin were validated with heparan sulfate. The dataset analyzed to compare heparin and heparan sulfate comprised 17 affinity pairs and 12 kinetic pairs. The majority of proteins (65%) interacted with a higher affinity with heparin than with heparan sulfate (Supplementary Fig. 2A ). 67% of proteins bound faster to heparin than to heparan sulfate (Supplementary Fig. 2B ) but most proteins (83%) dissociated more slowly from heparin than from heparan sulfate (Supplementary Fig. 2C ) suggesting that the experimental values determined for heparin should not be systematically extrapolated to heparan sulfate. In addition it would be more relevant to calculate the affinity and binding rates of proteins with heparan sulfate chains extracted from the tissues and cells of interest rather than to calculate these parameters with heparan sulfate chains from porcine intestinal mucosa or bovine kidney, which are commercially available.
The molecular functions, biological processes and KEGG pathways of each group were determined by in silico analysis using the DAVID database as described in Experimental procedures. Two molecular functions of proteins were associated with high affinity for heparin, enzyme-inhibitor activity (K D b 100 nM) and protein dimerization activity (K D = 10-100 nM, Fig. 4A ). Proteins with serinetype peptidase activity, cytokine and growth factor activities and proteins binding to calcium ions and extracellular matrix had moderate affinity for heparin (K D b 500 nM). To determine if enzymatic activity was associated with a given range of affinity for heparin we selected all proteins of the dataset with an Enzyme Commission number (Supplementary Table  1 ) and compared them to the other proteins of the dataset.
Affinity and binding kinetics of structural and regulatory interactions seem thus to be different and further differences exist for proteins involved in pathological processes such as cancer. Our dataset was representative of the heparin/heparan sulfate interactome since the GO biological terms and the KEGG pathways associated with the heparin-binding proteins of our dataset are those reported by Ori et al. for 435 human heparin-binding proteins  (Ori et al. , 2011). However further work is required to increase the coverage of protein annotations. Only 20% of the Fig. 3. Comparison of affinity and kinetics of heparin binding to full-length proteins and their fragments. Mean affinity (A), association (B) and dissociation (C) rates of heparinprotein interactions calculated by SPR (Supplementary Table  1 ).
Dashed lines: higher value for proteins, solid lines: lower values for proteins. A logarithmic scale was used for affinity and kinetic parameters. heparin-binding proteins of our dataset were annotated with the GO term "heparin-binding". When the entire dataset was considered, irrespective of protein categories, no obvious relationship was observed between the extent of intrinsic disorder in proteins and their affinities or binding kinetics to heparin. In contrast, we found that the disorder of heparin-binding sites is associated with an increase in the association rate in agreement with the report that the rate of macromolecular association is enhanced by the presence of disorder  (Shoemaker et al. , 2000; Huang and Liu, 2009). We also observed a trend towards a decrease in the dissociation rate and hence an increase in the stability of complexes formed by heparin with disordered proteins.
Context matters in vivo. Biological processes are context-dependent and interactions influence each other at various levels forming interaction networks. We focused here on the interaction network of endostatin, an endogenous inhibitor of angiogenesis  (Faye et al. , 2009a)  (Fig. 6 ). Endostatin interacts with another endogenous inhibitor of angiogenesis, endorepellin, which counteracts its anti-angiogenic Fig. 4. Molecular functions (A), biological processes (B) and KEGG pathways (C) of proteins ranked according to their affinity for heparin. The percentage of proteins annotated with GO terms for molecular functions and biological processes and for KEGG pathways were calculated in silico with the Database for Annotation, Visualization, and Integrated Discovery  (DAVID, Dennis et al. , 2003; Huang et al.
The dissociation rate is also useful to evaluate the half-life of complexes, which regulates, in association with affinity, the duration of the biological effect triggered by an interaction. As discussed by  Willis et al. (2013)  the evaluation of the in vivo concentrations of proteins and heparan sulfate are essential to make biological sense of measured affinities and to determine which interactions take place in vivo among all the possible interactions identified in vitro. The concentrations will also be useful to account for the opposite response of different endothelial cell types to endostatin depending on the concentration used  (Schmidt et al. , 2004). The concentrations of network participants, including endostatin, might be obtained either from quantitative proteomics datasets and/or inferred from transcriptomic data even if this option may not be the best choice.
Protein annotations Protein categories were those defined by Hynes group namely the core matrisome (ECM glycoproteins, collagens and proteoglycans) and matrisome-associated proteins comprising secreted proteins (e. g. growth factors and cytokines), ECM regulators (proteases, protease inhibitors and ECM crosslinking enzymes), and ECM-affiliated proteins (e. g. , mucins, C-type lectins, syndecans, glypicans, semaphorins, C1q, annexins, galectins)  (Hynes and Naba, 2012; Naba et al. , 2012). We added the following categories to account for the diversity of our protein dataset: membrane proteins, non-ECM secreted proteins, fragments (matricryptins, synthetic peptides), lipoproteins and nuclear proteins. Proteins were ranked into five classes according to their affinity for heparin (b 10 nM, 10-100 nM, 100-500 nM, 500-1000 nM and N1000 nM) for in silico functional analyses.
Gene Ontology (GO) terms (Biological Process and Molecular Function,  Ashburner et al. , 2000)  and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways  (Kanehisa et al. , 2012)  annotating heparin-binding proteins were determined with the Database for Annotation, Visualization, and Integrated Discovery, a bioinformatics resource aimed at systematically extracting biological meaning from large gene or protein lists (DAVID, http://david. abcc. ncifcrf. gov/, Dennis et al. , 2003; Huang et al. , 2009). Since heparin-binding proteins were annotated by numerous GO Biological Process terms, we selected one single representative term annotating at least 25% of the proteins in at least one affinity range of the dataset and we looked at the percentage of proteins annotated with this term in the other affinity ranges.
Prediction of intrinsic disorder in proteins Intrinsic disorder was predicted for heparin-binding proteins and for experimentally characterized binding sites to heparin. Putative binding sites were excluded from the analysis. We used the IUPred web server (http://iupred. enzim. hu/, Dosztányi et al. , 2005)  as previously described  (Peysselon et al. , 2011)  to predict the global percentage of disordered residues in the sequences of the heparin-binding proteins. Collagens and C1q, predicted as intrinsically disordered at the individual polypeptide levels  (Peysselon et al. , 2011)  but interacting with heparin as structured multimers, were not included in the predicted dataset. The endostatin network at the endothelial cell surface. Affinities of the interactions (b10 nM, 10-100 nM, 100-500 nM, 500-1000 nM and N1000 nM) are color-coded and visualized either as double arrows for the interactions of endostatin with its partners or as colored boxes for the interactions of endostatin partners with heparin/heparan sulfate.
The following structures were extracted from the Protein Data Bank (http://www. rcsb. org/pdb/): Endostatin PDB: 1BNL, endorepellin PDB: 3SH4, VEGF-VEGFR-2 complex PDB: 3V2A, αvβ3 integrin PDB: 1JV2, PDB: 2K9J (extracellular segment and transmembrane complex), MMP-14 PDB: 3C7X, MMP-2 PDB: 1CK7, Transglutaminase-2 PDB: 2Q3Z, nucleolin PDB: 1FJC, heparin PDB: 3IRK). ND: not determined. Affinity values were extracted from the references quoted in the text for the interactions of endostatin partners with heparin/heparan sulfate and from the following references for the interactions of endostatin with its partners:  Han et al. , 2012; Kim et al. , 2000; Sasaki et al. , 1998; Shi et al. , 2007.
(Hynes and Naba, 2012;Naba et al. , 2012)er protein category in the heparin-protein interaction dataset. Number of K D and kinetic parameter (association rate, ka, and dissociation rate, kd) values in our dataset of protein interactions with heparin, heparan sulfate and low molecular weight (LMW) heparin for each category of proteins as defined by Hynes group(Hynes and Naba, 2012;Naba et al. , 2012).
Acknowledgements We gratefully acknowledge  Sylvain Vallet  and  David Possamaï  ( UMR 5086 CNRS-Université Lyon 1, Lyon, France ) for their contribution to the first steps of the curation process,  Adriana E. Miele  ( Sapienza University of Rome, Italy ) and  David Hulmes  ( UMR 5305 CNRS-Université Lyon 1, Lyon, France ) for critical reading of the manuscript. This work was supported in part by a grant from the  Institut Rhône-Alpin des Systèmes Complexes IXXI  ( CNRS 073827 , 2011) and by the  European Commission  (PSIMEx, contract  FP7-HEALTH-2007-223411 ).
The presence of ERa and p85 was then analysed. The amount of ERa, p85 and PRMT1 were determined in the inputs. The lower panel shows the relative quantification of metERa/ERa. doi:10. 1371/journal. pone. 0087982. g002 Of note, the overexpression of JMJD6 reduced global asymmetrical dimethylation assessed with ASYM24 antibody (Figure  S7 ). Next, we identified putative JMJD6 partners by immunoprecipitation of MCF-7 transfected with JMJD6, coupled to mass spectrometry analysis. In agreement with data in the literature  [14, 19, 20]  most of the identified proteins were RNA binding proteins implicated in RNA processing. Interestingly, 23/25 proteins identified have been shown to be methylated on arginine residues (Table  1 ). Of interest, one of the identified putative partners of JMJD6 was the arginine methyltransferase CARM1, adding further evidence for its role in ADMA regulation.
Immunoprecipitation and Western Blotting To study the effect of oestrogen, the cells were treated for different times with E 2 (Sigma). After treatment, cells were lysed in RIPA buffer. Protein extracts were incubated with primary antibodies overnight at 4uC with shaking. Protein A-agarose or protein L-agarose beads were added and incubated 1 h at 4uC. The immunoprecipitates were separated on SDS-PAGE. The proteins were visualized by an enhanced chemiluminescence kit (Roche Molecular Biochemicals) following the manufacturer's instructions. For histone extraction, cells were lysed in a solution containing 3% of SDS and 10% of b-mercaptoethanol for 2 min at 95uC, then cooled in ice. After addition of DNAase, the samples were loaded on SDS-PAGE. Immunoblot images were digitized and quantified using the Image J Software.