dataesr/scientific-paragraphs-categorization huggingface dataset:
To cope with this problem, computational tools were developed to find candidate genes for these missing enzymes by using genome and metabolic context-based methods  [30] [31] [32]. The concept of these methods and the illustration of integrated tools using genomic and postgenomic data to link gene and function have been reviewed recently  [33]. Another illustration is presented through the MicroScope platform as a combination of CanOE and phylogenetic profile methods  [32, 34]. Actually, these in silico predictions have not raised a lot of orphan cases despite the sophistication of the methods and their relative independence from classical sequence based methods. As many orphan enzymes (1,223 reactions in KEGG and 915 in MetaCyc) have pathway neighbors that are orphans themselves, one difficulty is the definition of correct genomic contexts including candidate genes and known enzymes.
Nevertheless, the aspartate 4-decarboxylase activity has been characterized in various mammalians (e. g. rat, pig, chicken)  [38] , making the EC number 4. 1. 1. 12 a local orphan activity in eukaryotes. For the Archaea, there is no associated sequence and no literature evidence of its presence in this superkingdom. Thus, the aspartate 4-decarboxylase activity could be considered as absent in the Archaea. To conduct a survey of local orphans, a resource of characterized activities in identified organisms is required and should be exhaustive enough to gather all the biochemical knowledge published in the past century. We used the BRaunschweig ENzyme DAtabase (BRENDA, version 2013), which is one of the major public resources on enzymes and enzymatic activities, and contains a very large spectrum of information related to them  [39].
Nevertheless, a high proportion of reported orphans in Archaea (77%) are local orphans, suggesting either homolog proteins could be candidates for these activities or specific isoenzymes have emerged during their evolution. A similar analysis was conducted by adding FRENDA/AMENDA data (Additional file 1: Figure  S1. 2). Surprisingly, the number of orphan enzymes considerably increased in each superkingdom with a high proportion of local orphans (52% for Eukaryota and Bacteria, and 91% for Archaea). These results should be taken with caution as FRENDA/ AMENDA data is not subjected to manual curation (e. g. we found false-positive local orphans for Bacteria that correspond to heterologous expressions of eukaryotic proteins in Escherichia coli BL21). Nevertheless, this analysis demonstrates that, in addition to the 22.
Secondly, candidate homologous proteins may exist for local orphans but remain to be experimentally confirmed and annotated in databanks. To address this second point, we conducted a preliminary analysis to find homologous proteins for all local orphan enzymes in a given superkingdom. For that purpose, we applied the PRIAM software (release of March 2013)  [41]  against all UniProt proteins from the Eukaryota, Bacteria and Archaea superkingdoms (see Additional file 1: Figure  S1. 3). PRIAM relies on a set of profiles (i. e. position-specific scoring matrices), which are supposed to be characteristic of protein modules sharing same enzyme activities (i. e. same EC numbers). We found that PRIAM is able to retrieve candidate proteins for a non-negligible fraction of local orphans previously defined using BRENDA data: 30% for Archaea and Bacteria, and 59% in Eukaryota (Table  2 ; the lists of candidate proteins for local orphan and not observed EC numbers are available in Additional file 3: Tables  S3.
This suggests that the set of common enzymes between Bacteria and Eukaryota may be underappreciated in protein databanks and could be partially solved by a curation effort of eukaryotic genome annotations. As already illustrated, comparative genomics analyses between prokaryotes and eukaryotes are successful in finding common and specific enzymes in shared pathways  [20]. These homology-based predictions of enzymatic functions could be also completed by probabilistic annotation of metabolic networks to increase the accuracy of this strategy  [42].
Another origin of this catalytic promiscuity is the special case of moonlighting enzymes  [45]. These proteins switch between activities under environmental changes according to their cellular localization, expression in a novel cell type, ligand or cofactor concentrations, oligomerization or complex formation with other proteins. A repository of multitasking proteins was recently set up and several examples of moonlighting enzymes may be explored  [46]. The proportion of multifunctional enzymes may be underestimated  [47, 48]  and only a few enzymes are described as multifunctional in databases: among the ~250,000 enzymes in Swiss-Prot, 5% are associated with two or more EC numbers and 3% with EC numbers having different classification at third-level. This proportion should dramatically increase when we will find a simpler way to detect them.
For the global ones, a delay of knowledge in databases still exists and could be resolved by intensive bibliographical searches. In this way, the Orphan Enzyme Project initiative  [56]  recently conducted a systematic analysis of databases and publications, and found protein sequences for about 270 presumed orphans among an initial list of 1,122 activities established in 2009  [16]. Similarly to what is done for protein structures with the PDB  [57]  and nucleic sequences by the INSDC (International Nucleotide Sequence Database Collaboration)  [58] , the design of a central and common scientific framework to submit enzymes with their activities is of priority to reduce the loss of knowledge between publications and databases. Indeed, collaborative initiatives were recently established to discover new activities and enzymes: the Enzyme Function Initiative  [59]  which addresses the challenge of assigning reliable functions to enzymes discovered in bacterial genome projects, and the COMBREX project  [60] , connecting computational and experimental biologists to improve protein annotation and proposing grants to experimentally validate new functions.
Reviewer 1 (First Round): Dr. Michael Galperin The paper by Sorokina et al. addresses an important question and includes some interesting results. However, I think that in order to justify publication in Biology Direct, the paper needs to be much better written. The current version is something intermediate between a review and a regular research paper and does not make for either a good review or a good research paper. As an example, I would suggest moving Figure  1  to Supplementary Materials (it is not a new result) and moving Figure  S2  into the main text (it is a new result). Authors' response: Our article is not a regular research article but a review paper written in a format similar to previous studies listed in Figure  1. It includes updated analyses of existing data from public databanks that substantially enhance our knowledge about orphan enzymes.
We thus decided not to move Figure  1  to Supplementary Materials as it resumes previous studies. Figure  S2  (now S1. 2) is an estimation of orphan enzymes at the superkingdom level based on non-curated data from FRENDA and AMENDA whereas Figure  6  was made using manually curated data. Therefore, we prefer not to move Figure  S1. 2 to the main text. In addition, I am afraid that the current version of the manuscript does not really benefit the scientific community as it simply enumerates the enzymes in each category without providing the specific lists of these enzymes. I could support publication of this paper only after the authors include (at least as Supplementary Materials) the lists of global and local orphans from Figure  S2. Unless this is done, the data in Figures 2, 3 and 4 cannot be independently verified and the entire manuscript cannot be considered acceptable for publication.
We agree that PRIAM profiles have limitations but, as far as we know, it is one of the best tools to track potential conserved domains which are enzyme specific and have a wide coverage of Swiss-Prot enzymes. BBH cannot be computed for all the Swiss-Prot enzymes as many of them are not from complete organisms. As mentioned in the manuscript: "these [PRIAM] predictions cannot be transferred directly without supplementary bioinformatics analyses or experiments". As a review, the manuscript did not do justice to the methods that might be used to find orphan enzymes in general, or domain orphans. In particular, Yamada et al. (ref 27) struck me as a landmark demonstration of data mining combined with comparative genomics for finding complete sequence orphans. The method would work even better for superkingdom orphans.
Does the article title, "Profiling the Orphan Enzymes", refer to PRIAM profiles as used in the untested workflow? If so, a revised title might be more appropriate. Authors' response: The title is not related to PRIAM profiles. The aim of our review is to analyze and discuss the orphan enzyme problem in the light of the current knowledge in public databanks. These lists could be very useful for future studies. My only concern is with the confusing terminology used to name the enzyme groups. The authors use the term "missing enzymes" for the enzymes that are absent (not encoded), rather than missing (not found), in the given taxonomic group. Instead, they use the term "local orphans" for the enzymes that everybody else in the world refers to as "missing enzymes". 1. Enzymes (EC numbers) that are not associated with any sequences are referred to as "global orphans" even though many (probably most) of these enzymes have been described in a single species, or a group of closely related species, and therefore represent "lineage-specific orphans", rather than "global orphans".
Please either delete or at least reformulate. Authors' response: Part of the abstract has been rewritten according to the reviewer suggestions. 4. The Introduction could (and should) be made more compact and succinct. That said, the last paragraph of the Introduction contains a much better description of the work presented in this paper than the Abstract does. Authors' response: We removed the definition of the EC nomenclature but we think that it is important to keep a description of previous analysis reviews on orphan enzymes in the introduction. 5. Citations of the enzyme and EC number databases in the Introduction and other sections of the paper present are unfortunately biased. The authors should, at the very least acknowledge the official web sites of the EC classification, the IUBMB list (http://www.
Theory and simulations predict Quasi-Periodic Oscillations of shocks which develop in magnetically driven accretion funnels connecting the stellar disc to the photosphere of Young Stellar Objects (YSO). X-ray observations however do not show evidence of the expected periodicity. We examine here, in a first attempt, the influence of radiative transfer on the evolution of material impinging on a dynamically heated stellar atmosphere, using the 1D ALE-RHD code ASTROLABE. The mechanical shock heating mechanism of the chromosphere only slightly perturbs the flow. We also show that, since the impacting flow, and especially the part which penetrates into the chromosphere, is not treated as a purely radiating transparent medium, a sufficiently efficient coupling between gas and radiation may affect or even suppress the oscillations of the shocked column.
Hydro-radiative simulations with the code ASTROLABE Computing accretion can be performed through two main approaches: on the one hand, 2D and 3D simulations can include the influence of the magnetic field  [9]  as well as geometric or flow inhomogeneities effects  [10, 11] ; on the other hand, 1D simulations can hardly deal with these effects  1  , but may include more refined physics, like time-dependent (non-LTE) ionization or radiative transfer. Our study is based on simulations performed with the 1D ALE code ASTROLABE  [12]  developed by one of us (J. -P. C. ). This code solves the coupled equations of radiative hydrodynamics, written in the comoving frame. We consider here a system made of a plasma composed of H, H + , He, He + , He 2+ , M and M + , where M represents a "catch-all metal" which completes, with the free electrons, the composition.
com. ]  and SYNSPEC  [32]. Another one is to use more physical escape probabilities  [33]. Radiation parameters in this work (energy, flux and opacities) are integrated over all frequencies. The underlying assumption, i. e. the spectral radiation energy distribution is a Planckian, is valid in the visible range, where photospheric emission dominates, but not in X rays, which include accretion shocks signature. An ongoing revision is to distinguish these radiation parameters in several frequency groups (visible+IR, UV, X) to take into account radiation specificities in each of them. This work is supported by french ANR, under grant 08-BLAN-0263-07, Université Pierre et Marie Curie, Observatoire de Paris and CEA Saclay.
INTRODUCTION Epidemiology studies require valid and reliable data in order to determine appropriate strategies for the control of specific indicators  [1]. In order to ensure that a control strategy is effective and appropriate, the data need to be of high quality. Consequently, epidemiology requires a rigorous and systematic approach to data management. The importance of data management seems often underestimated, with greater emphasis placed on the study design, data collection, and data analysis. This can result in an ad-hoc approach to data management that ultimately affects the reliability and validity of the data collected and increases the workload involved in data curation. Since 2010, the GINSENG project (e-ginseng. org) has grouped IT researchers and physicians in order to create a proof-of-concept network for epidemiology in Auvergne dedicated to cancer surveillance and perinatal health.
Presently, patient records are faxed or delivered using a simplified e-messaging between healthcare professionals to be duplicated in their databases. This process is not efficient, time consuming and can be responsible for errors caused by a dual registration of the medical information. Otherwise, interactions between healthcare professionals remain ineffective for the patient follow-up, mainly because of a lack of interoperability between information systems. Furthermore, the French Health Watch Institute (InVS), equivalent to CDC (Centers for Disease Control and Prevention) for the USA, is in charge of publishing indicators about population health and particularly about cancer incidence  [2] ,  [3] ,  [4] ,  [5]. To produce such indicators, InVS relies on regional data warehouses set up to collect relevant information to support statistical and epidemiological studies about cancer incidence, mortality, prevalence or screening  [6].
Until now, cancer registries remain few and not easily interoperable with other medical databases. The GINSENG project develops a dedicated grid infrastructure to support large-scale epidemiology analysis based on distributed, heterogeneous and confidential medical data sources (hospitals, cancer screening associations, cancer centers and pathology labs, etc. ). It addresses the wellidentified problems of increasing the number of interesting medical variables and observations, improving the representativeness of the data (typological and geographical), and maximizing the validity of information  [7] ,  [8]. This objective does not require nominative patient data even if unambiguity on patient identification and data ownership is necessary to correlate data entries. Data interoperability can be greatly enhanced by using a semantic approach to define, describe information to exchange and facilitate the exploitation of data available in multiple data sources  [9] ,  [10].
A. Global infrastructure The GINSENG Network architecture is detailed in Fig. 1. It adopts a flexible Service-Oriented Architecture (SOA) to harness the complexity of such a distributed system. It federates different medical databases, from pathology lab cancer screening to PACS systems, etc. Each database is duplicated on a grid server (gateway) located at the home institution. An administration server is in charge of managing user authentication and security features. Users are authenticated using their French professional health cards identifiers. This way, physicians can log in the GINSENG network with their unique professional ID, and transparently access to distributed computing resources by means of credential delegation. Each gateway is organized in different layers of services: x The Anonymization Service offers various data anonymization possibilities according to medical data types being manipulated within the system (e.
g. DICOM headers blanking/encryption/removal, image blanking/scrambling etc). x The Grid Interface Service bridges with all other grid middleware services such as computational services. x The Database Interface Service bridges with all XML and relational database back-ends. Storage management and data access are managed by AMGA (ARDA Metadata Grid Application)  [14]. AMGA is installed as an additional layer on top of a DataBase Management System (DBMS), usually MySQL, and provides a structured tree to manage folders with flexible access control mechanisms for individual data items based on Access Control Lists (ACLs).
B. Specificities for medical data management Each stakeholder of the GINSENG project owns a grid server hosting two kinds of services: The medical data upload and standardization service automatically uploads the medical database. Each time the database is uploaded (usually out of working hours, once a month), new patients are identified and specific IDs are allocated for their registration in the GINSENG network  [15]. In order to control patient duplicates, a patient identification technique, based on a probabilistic record linkage method taking into account field similarity in the calculation of field weights  [16] ,  [17]  has been implemented. The data is then structured through the FedEHR format (see section 3. A) in order to be queried via the web portal e-ginseng. org. The medical data query service allows external users to query the grid server, according to the local security and authentication policy fixed by VOMS.
A. Electronic Health Record implementation FedEHR, the Federated Electronic Health Record data warehouse platform has been developed to store a large variety of medical concepts. FedEHR allows building vendor-neutral and cloud-enabled patient-centric EHR data warehouses. It has been primarily designed for biomedical research. FedEHR makes it possible to add medical data according to their original format or standards such as the Systematized Nomenclature of Medicine (SNOMED-CT) 1  or the International Classification of Diseases (ICD-10) 2. Based on grid/cloud technologies, it provides an extensible repository for patient data. When queried, the FedEHR service can behave as a centralized database giving access to all the databases connected on the network. The EHR standard provides a metamodel made of general entities common to all medical fields: the Patient, the Visit, the MedicalEvent and the ClinicalVariable.
Those four entities are organized hierarchically in order to represent the medical history of the patient: each patient undergoes visits, each visit is related to medical events and each medical event is a clinical dataset. It has to be noted that a clinical variable can be linked to a clinical variable dataset. The mapping between medical datasets and FedEHR can be settled using an XML language in order to specify more information on data types. Those medical events, referenced in different medical databases are glued together using a FedEHR organization after patient identification.
B. Medical database queries FedEHR implements an AMGA-based multi-tier repository for data stored in XDS (Cross-enterprise Document Sharing), XDS-I (Cross-Document Sharing for Imaging), DICOM (Digital Imaging and Communications in Medicine), and HL-7 (Health Level Seven) standards, and can handle any other data type. It combines the abstraction power of AMGA together with a proprietary big data technology to provide a powerful data warehouse to securely share medical information across the healthcare enterprise and transnationally, while preserving patient privacy and data copyrights. Using FedEHR, federated data sources are replicated locally, transformed and made available to the GINSENG network. Using AMGA, FedEHR behaves in each center as a standard SQL database. Inherited from this architecture, AMGA SQL queries can be performed through FedEHR web services returning an aggregation of results from each data center.
This solution carries a serious drawback when information about one patient is distributed in different sites. Using standard AMGA queries, distributed patient information fragments are not properly joined and the result of a query could be incomplete. To address this issue, FedEHR provides a former technology called SQL². Based on the structured medical database, involving clinical variables depending of medical events, FedEHR is performing SQL queries at two levels. A first set of queries is executed following the methodology of a standard AMGA query on the different metadata (Medical Events or Clinical Variables). Using result of these first level queries, temporary tables are created on a queryenabled computing element, which can be either a physical server, a virtual machine, instantiated in a cloud or a job in a grid enabled environment.
Once created, these tables enable standard SQL query to be run on the new database. This query system is a simple and valuable solution avoiding data replication around data centers and ensuring data property to providers. From a user standpoint, clinical variables are presented through a specific GUI proposing a standard SQL query interface (Fig. 2 ). From a technical standpoint, for each SQL² query, only the needed data is transferred through a secured encrypted protocol to the computing element sending the query. This reduces bandwidth consumption and ensures a high level of security. Thanks to FedEHR, GINSENG carries and extends the Grid intrinsic capabilities to address the complex and demanding requirements of health surveillance. FedEHR makes it possible to extend GINSENG to other medical fields.
A. Knowledge graphs based on electronic health records To properly align heterogeneous data stores with widely accepted and well-understood concepts, the data semantics should be taken into account. The Semantic Web  3  addresses this problem with a rich set of widely accepted standards for attaching semantics to data and linking data sets (RDF), describing common concepts (RDFS and OWL), and enabling rich querying (SPARQL query language). RDF (Resource Description Framework) represents distributed identities and concepts as a uniform directed and typed graph even if entities are located on different knowledge bases. It describes resources with triples (subject, predicate and object) that can be viewed as "the subject, verb and object of an elementary sentence", "a natural way to describe the vast majority of the data processed by machines"  [18].
Unique Resource Identifiers (URIs) are used to uniformly identify entities and concepts of each description, linking descriptions and models across knowledge bases. Thereby, RDF allows publishing medical data from any autonomous infrastructure, while enabling to interconnect whole distributed datasets as a uniform Linked Data structure  [19]. RDFS (Resource Description Framework Schema) and OWL (Ontology Web Language) are RDF-based standards to define ontologies. An ontology is defined by  [20]  as "a set of representational primitives with which to model a domain knowledge". The representational primitives are typically classes, attributes, and relationships among class members. The definitions of the representational primitives include information about their meaning and constraints on their logically consistent application.