dataesr/scientific-paragraphs-categorization huggingface dataset:
Drawbacks and limitations of the MIDAS prototype Current known drawbacks of the MIDAS prototype concern manageability and usability issues. We plan to develop productivity tools such as wizards for the Test Configuration Model and the Service Behaviour Model, to improve the consoles and dashboards for test generation and execution and also to implement a Test Data Management System (including version management) that allows for storage and query of all models, policies, test suites, logs, reports and other artefacts (as well as all links between these artefacts) that are consumed and produced by each test generation and each test running session. In terms of matching test outcomes with oracles, the current approach performs a complete matching over the corresponding SOAP messages (active oracles).
The cumulated effort spent for the test campaigns (building and analysis) amounts to 50 person/days. It is important to note that this cost has been measured starting from the availability of a running and stable version of the MIDAS prototype and does not take into account any iterations during the MIDAS project due to the deployment of alpha and beta versions of the prototype and the beta testing activity performed by the Dedalus team on the prototype itself. This measure of the costs in terms of person/days is objective and has supported a more general re-assessment of the costs of installing and deploying a software product of the complexity of the Healthcare Pilot, and of the expected revenue and profit, in a situation in which the MIDAS testing technology was available. This assessment can be completed only by considering the performance indicators as well.
Performance indicators The considered key performance indicators (KPI) are: -Efficiency -degree of optimization of the engineering process from the development up to the delivery and maintenance phases; -Effectiveness -number of defects that can be discovered and the distribution of the discovery in the phases of the engineering cycle (the earlier in the cycle, the higher the effectiveness indicator). The CCN project is a typical Dedalus customer project. In its study, Dedalus has compared its Economic Evaluation Sheet with other three customer provisions using similar technology (X1. V1, the Dedalus software product implementing the aforementioned services [13]) and the same economic quotation. In all of these scenarios the estimated costs for the configuration, distribution, installation and operation of the solution sum up to 50% of the total value of the provision itself and the corresponding effort amounts to about 550 person/days.
More precisely, the activities that contribute to this cost are: (i) software tuning and configuration, (ii) integration with third party software (target of the MIDAS technology), (iii) internal testing, (iv) integration testing of the overall architecture (target of the MIDAS technology), (v) training, and (vi) delivery and operation. The integration testing activity evaluates to around 15% and the corresponding effort is about 80 person/days. When compared to the aforementioned cost of 50 person/days, the reduction of more than 35% for this activity is significant. Effective model driven and automated SOA testing approaches also have an impact on the costs for the integration with third party software, which is evaluated to 35% of the total cost with a corresponding effort of about 190 person/days.
Quality indicators The quality indicators taken into account are: (i) the confidence in software quality; (ii) the capability to reveal the majority of failures not later than the integration testing phase; (iii) the reduction of delays. In terms of confidence in software quality, Dedalus estimates that the use of the MIDAS technology would increase the level of trustworthiness to "very confident" in about 90% of cases, considering that the "confident" level is necessary to embrace novel and less conservative business models. In the past a great number of failures were discovered during the user acceptance phase or while the software was already in production. Dedalus estimates that the MIDAS technology has the firm potential to concentrate the discovery of defects in the early stages, and to significantly reduce the cost of late defect discovery.
Licensing As the MIDAS prototype is a SaaS, there is no specific licensing issue for the user as it would be for traditional software installed on premises. The end user basically has two access points to MIDAS: through a Web application accessible from a browser, or through APIs (today SOAP Web services). An industrial SaaS, simplyTestify, is being developed by Simple Engineering, a start-up partner of the MIDAS project that will run an early adopter free trial programme beginning the last quarter of 2016 [14], and the fully accessible commercial offer, on a pay-as-you-go basis including free tier, in the course of 2017.
I. INTRODUCTION The increasing complexity of modern applications, executed on parallel architectures often dictates an efficient decomposition of the computational load in order to ensure high performance. In literature, many applications that arise in scientific computing  [1] , circuit design  [2]  or database modeling  [3]  use graph theory to describe and solve the problem of distributing computations to a number of available processors. More precisely in a graph, a vertex represents a computation while an edge represents data dependencies between computations. Additionally, weights may be assigned to vertices and edges to further quantify their values. Hence, the problem of distributing computations becomes the partitioning problem of how to divide vertices of a graph in k parts of roughly equal size, such that the number of edges connecting vertices in different parts is minimized (edgecut minimization).
For the sake of brevity and readability of our experimental results, each bar of the charts in Figures  4  and 5  represents the average value of each result for all graphs in our data set. Additionally, note that each experiment is performed 10 times for every graph. In the charts, we present results on edgecut and execution time, while the number of desired parts increases from 10 to 100. In the first experiment (Fig. 4 ), we compare the KGGGP algorithms to SCOTCH and KMETIS on the classic graph partitioning problem, that is without using initial fixed vertices. The purpose of this experiment is to exhibit that even though KGGGP is not the best algorithm for the classic problem, the results we obtain are quite good. More precisely, KMETIS produces partitions with minimized edgecut followed by SCOTCH with a 5% increase of edgecut which overall, confirms (as expected) that RB based methods perform better than the greedy ones for this problem.
For example, the bilaterian species A. californica was omitted from downstream analyses because of its current low quality protein annotation. It is also possible that truly existing genes are missing from the non-bilaterian dataset due to annotation or prediction errors, leading to wrong inferences of bilaterian specificity. This error, however, is likely small because we interpret a pattern of presence and absence across several species, thereby reducing the impact of individual mistakes. Another limitation is the restriction to 16 genomes from which we infer bilaterian specificity. Although this is a very small fraction of the known metazoans, it contains all non-bilaterian genomes available at the time of the analysis and it constitutes a representative selection of bilaterian genomes.
elegans, D. melanogaster, D. rerio) are represented in all clusters. On average, a (bilaterian) species is represented in 93. 1% of the clusters. Non-bilaterian species which are not present in our initial dataset might possess true orthologs that we are not able to detect. To rule out this possibility, we verified by additional BLAST searches at NCBI that none of the 85 COPs had BLAST hits below an E-value of 10 -5 in non-bilaterians or other eukaryotes (suppl Table  S4 ). Again, non-orthologous gene displacement (NOGD) may obscure the fact that functional orthologs may escape detection when only working with sequence similarities. As a result of our experimental design, but also due to different duplication histories, COPs contain differing numbers of paralogs from different species. On average we found most paralogs in Deuterostomia, followed by Ecdysozoa and Lophotrochozoa.
To our knowledge, this novel method is the only test that is sensitive to coherent enrichment of GO terms across multiple species (see Methods). Applying MSGEA we extracted a list of GO terms which are likely associated to long-term retained functions of our candidate genes. Concentrating on the domain 'biological process' of the GO database, we find the terms 'development', 'muscle', 'neuron', 'signaling' and 'regulation' to be strongly over-represented (Fig. 3 ; suppl. Fig. S1 ; suppl. Table  S6 ). This is compatible with the idea that key bilaterian innovations involved the central nervous system, muscles and complex cell types and tissues. The terms identified through in silico analysis confirmed our hypothesis that prime candidates for bilaterian specific genes must be those which are acting during development.
Extraction of biological function through human curated literature mining Since orthology is a strictly phylogenetic criterion, and since GO classification draws heavily on sequence homology, without necessarily reflecting conserved function, we decided to perform an in-depth humancurated literature search to collect additional evidence for the functional role of genes in COP clusters. We mined the literature databases and compiled human curated functional descriptions, based on experimental evidence, for the proteins contained in set L. We defined the six classes 'Neuron related', 'Morphology related', 'Muscle related', 'Signaling related', 'Regulation related' and 'Others' (suppl. Table  S6 ). We used functional descriptions extracted from the literature to assign each of the 85 clusters to one of these six classes (Fig.
3 ). The manual extraction of species-and protein-specific functional information allowed us to compare and better interpret functions across the three model organisms. To compare the human-curated classification with the one based on the GO database, we assigned all GO terms appearing in set L to one of the six classes above. Since a given protein may be associated with many GO terms there is no one-to-one relationship of the human curated annotation and the GO terms. For example, while the human curation assigns eight of the 85 clusters (the left-most columns in  Fig 3)  to the category 'Morphology related', morphology related GO terms occur in 33 clusters, scattered throughout the six classes. Similarly, signaling related GO terms are found in almost every cluster, while the class 'Signaling related' contains only 18 clusters.
Many of these neurogenic factors are present in poriferans and cnidarians and thus predate the evolution of bilaterians  [74]. However, additional factors evolved in the ancestor of bilaterians when various neurogenic gene families were subject to another round of gene amplification (eg. Pax3/7, Engrailed, SoxA, SoxD, Neurogenin, Eomes)  [74]. Our screen could not recover the majority of these known bilaterian-specific neurogenic factors. As they often originated from duplication events, their domains are inevitably similar to the domains present in the respective pre-bilaterian founder gene. A similarity above threshold leads to the exclusion of such genes from the dataset according to our filtering rules. Prospero, on the other hand, contains a highly divergent atypical homeodomain attached to a prospero DNA-binding domain (Homeo-prospero domain, pfam accession PF05044), a combination that is found only in bilaterians (http://pfam.
xfam. org/ family/PF05044#tabview=tab7). Because of its unusual structural features, prospero is different from non-bilaterian homeodomain proteins and could be retained in the dataset. This result is in agreement with earlier studies that failed to detect homeodomain proteins of the PROS (prospero) class in cnidarians and ctenophores  [75, 76]. During nervous system development of cnidarians, three types of neurons arise from multipotent interstitial stem cells in Hydra  [77]  or from endoderm/ectoderm in Nematostella  [78]. In contrast, neurogenesis in bilaterians is characterized by cell lineage expansion and diversification, involving multiple factors and dedicated neural progenitor cells  [79]. Our results suggest that the bilaterian-specific homeodomain transcription factor prospero contributed to the sequence of nerve cell specifications via progenitor cells that is characteristic for bilaterians, and thus to the boost of complexity accompanying nervous system development in these animals.
Materials and Methods Using a comparative genomic approach, we identified genes shared among bilateria. To represent the spectrum of the different bilaterian clades we have selected species from the three major clades -Deuterostomia, Ecdysozoa and Lophotrochozoa -concentrating on species with a fully sequenced genome (Table  1 ). We included more species from Lophotrochozoa than from the other two clades, since wellannotated complete genome sequences from Lophotrochozoa model-organisms were still under-represented in public databases at the beginning of our studies. To obtain a high contrast between bilateria and non-bilateria, we used seven non-bilaterian species, derived from a previous study  [22] , as an exclusion criterion for the further analysis. Proteomes and data from InterProScan  [95]  and the Gene Ontology consortium  [96]  were retrieved from the sources cited in Table  1.
Third, we grouped the ortholog clusters into four sets (A, L, M , C; Fig 1 ), satisfying different additional stringency conditions. Due to the poor sequence quality, we treated the Lophotrochozoon A. californica differently: where available we included A. californica orthologs, but we did not require them to be present in set A ('all species'). The above data sets are ordered by degree of confidence in the bilaterian specificity of the protein. At the same time they reflect the level of conservation of the proteins across different species. Seeking to be conservative regarding bilaterian specificity on the one hand (set A being most conservative), but also to analyze a dataset which is as comprehensive as possible, we focused our further analyses on the intersection of sets L and M (called L ; Fig  1 ).
Multi-Species Gene Enrichment Analysis To examine function of the genes in set L by in silico methods, we obtained their gene ontology (GO) terms for the three model species from the GO database  [96]. To identify terms enriched in bilaterian specific genes compared to the genomic background, we employed two strategies: the conventional single-species enrichment analysis based on Fisher's exact test and a novel method, termed multi-species gene enrichment analysis (MSGEA) (manuscript in preparation). Standard single-species Fisher's exact tests for enrichment cannot capture a signal of moderate, but joint, enrichment across different species. Instead, with MSGEA we compute an exact p-value for a pooled set of species. It works as follows. First, for each GO term we counted the number of descendants in the GO tree.
Detailed functional analysis of genes from model organisms We employed biomaRt to mine literature databases for functional studies in model organisms with a focus on development: we used the Bioconductor module [101] biomaRt 2. 19. 3 to extract information for proteins present in cluster set L'. Wormbase release WS220 was queried for C. elegans proteins and ENSEMBL 75 for D. melanogaster and D. rerio proteins. We then searched extensively the literature for experimental validations of protein function. We grouped the results in six major categories, described in Results and Discussion and summarized in suppl. Table  S6. These categories represent prominent molecular functions during embryogenesis and development. Based on the retrieved annotations we assigned proteins in set L' to these categories.
The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing.
Methods The Recon2 human metabolic reconstruction (in SBML format) was integrated into the Neo4j framework (https://neo4j. com/), which uses a graph database approach. The major concepts involved in the metabolic reactions (metabolites, proteins, complexes and metabolic reaction names) were represented as nodes in the graph database, while the relationships among them (e. g. consumption, production, catalysis) as connecting edges. In addition, the relationships between the compounds (nodes) and the complexes were represented by 'part of' edges. Information on the name, the SBO Term identifier and additional details (such as initial concentration, charge, metadata) were stored as attributes (properties) of the nodes. An SBML species was classified as a node of either a metabolite, a protein or a biological complex based on its SBO Term identifier in the Recon2 input file.
For the proteins and biological complexes nodes, the UniProt identifier information was also stored as node attributes. When available, data related to biological compartments (including compartment name, meta id, SBO Term id, size, spatial dimensions) were also stored as attributes for every species node. For the metabolic reactions, information such as name, identifier, metadata, notes, the reversibility property, were stored as attributes of the Reaction nodes; for the consumption and production reactions, the stoichiometric relationships were also captured as edge properties. The Neo4j-based metabolic representation of Recon2 is composed of i) nodes: 5063 metabolites, 3567 proteins, 7440 metabolic reactions and 1168 complexes (with 590 protein compounds); and ii) relationships (edges): 15677 consumption, 15863 production, 9982 catalysis and 590 partof relationships between complexes and their compounds.
The data graph model of the Neo4j-based metabolic framework is given in Supplementary Figure  S1  (Supplementary file S1). A parser component was developed to convert the query results from the Neo4j-based metabolic framework in the JavaScript Object Notation (JSON) format to the SBML standard format and the Standard Interchange Format (the SIF format), compatible with well-established environments for biological data management (e. g. Cytoscape  (Smoot et al. , 2011) ) and network sharing (e. g. NDEx  (Pratt et al. , 2015) ). Both the Neo4j-based metabolic framework and the parser component were developed mainly in Java using: the JSBML 1. 0 library  (Dr€ ager et al. , 2011)  for managing the SBML files (read and write data, check consistency of the SBML output), the Neo4j Java API to build the Neo4j-based resource and the JSONsimple 1.
Results The developments presented here focus on two major components: i) a Neo4j graph database for the human metabolism data and ii) a Java-based parser for translating the JSON representation of the Neo4j networks into the SBML and SIF formats. The major steps of the overall workflow are illustrated in Supplementary Figure  S2  (Supplementary file S2) and are described briefly below. Firstly, the Neo4j-based metabolic framework facilitates exploration and visualization of the human metabolic network. As an example of exploring the newly developed resource using the Neo4j Cypher declarative language, a use-case was developed to identify pathways and subnetworks useful for understanding the metabolism of the arachidonic acid, a metabolite that plays a crucial role in inflammation processes.
The metabolic network shown in Supplementary Figure  S3  (Supplementary file S3) identifies metabolites and proteins three metabolic reaction steps away from the arachidonic acid (or, in terms of nodes in the graph, the figure illustrates the 6-steps neighborhood of the arachidonic acid node). The network from Supplementary Figure  S3  excluded paths with highly connected promiscuous nodes (such as those representing the 'proton', 'H 2 O', 'Sodium'), to avoid having all nodes interconnected. A list of examples of Cypher queries for the metabolic framework (including the query for Supplementary Fig. S3 ) is given in Supplementary file S4. Second, the user can import the Neo4j output file (the JSON format), which contains data on the metabolic subnetwork identified using a Cypher query (e. g.
Thus, a powerful feature of the Recon2Neo4j framework is facilitating querying and exploration of integrated metabolic data (via the Cypher language, as discussed above), which adds to the functionality provided by other systems biology software, such as cySBML  (Ko ¨nig et al. , 2012). Recon2Neo4j can be easily extended to process other input files available in the SBML standard format, due to the use of the JSBML library functionalities to manage the SBML files, and also to integrate new data types if these become available, due to the use of the graph database approach that presents schema free properties. (More detailed discussions on using the Neo4j environment for the management of biological and biomedical data can be found in e. g. (Lysenko et al. , 2016) ). As possible future development steps, it would be useful to add more information to the metabolic network, such as synonyms for metabolite names and tissue expression level for proteins (e.
Funding The research leading to these results has received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement no. IMI 115446  (eTRIKS), resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies in kind contribution. AL and CJR acknowledge support from the  BBSRC  of the UK through their strategic funding of  Rothamsted Research.
The use of domain-specific languages (DSLs) is a successful technique in the development of complex systems. Indeed, the construction of new DSLs addressing the particular needs of software projects has become a recurrent activity. In this context, the phenomenon of specification cloning has started to appear. Language designers often copy&paste some parts of the specification from legacy DSLs to "reuse" formerly defined language constructs. As well known, this type of practices introduce problems such as bugs propagation, thus increasing of maintenance costs. In this paper, we present Puzzle, a tool that uses static analysis to facilitate the detection of specification clones in DSLs implemented under the executable metamodeling paradigm. Puzzle also enables the extraction specification clones as reusable language modules that can be later used to build up new DSLs.