dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on our analysis of terminological solutions, we investigated whether LexEVS was a functional tool to implement this approach. In the next section, we present the framework and describe the generic approach for each of its components. We then test this method on a clinical study example from the TRANSFoRm project, focusing on integrating two primary care data repositories, the NIVEL primary care database (NPCD)  50  of the Netherlands Institute for Health Services Research (NIVEL)  51  and the general practice research database (GPRD)  52  of the UK's Medicines and Healthcare Products Regulatory Agency. 53.
The mediation engine generates a specific query for each data source 3. The data sources fulfill the requests 4. The returned dataset has its structure aligned with the GIM -DSM to extract which terminology was used to code a given concept in the source 5. If possible and desired, the system can semantically align resulting coded values based on the terminologies used by one of the sources or a separate terminology. This operation uses: -Terminologies and mappings between terminologies to transcode the values.
Mappings between terminologies Once terminologies are loaded in LexEVS, mappings between them can be created in a similar way as for the data models. For some of them, relationships are readily available and can be simply loaded into LexEVS. This is typically the case for terminologies integrated in the UMLS. For others, local mappings have to be created. For example, if a hospital uses a local coding set to identify its laboratory tests, it could be loaded into LexEVS. Subsequently, mappings between this local set and logical observation identifiers names and codes could be created. This would allow translations from the local site to a more standard terminology, thereby facilitating interoperability with other groups without having to recode data locally or create a duplicate data warehouse.
RESULTS The first implementation of GIM was realized as part of the EU FP7 TRANSFoRm project, which aims at supporting patient safety through integration of clinical and research settings, workflows and data. 11 The technology developed can facilitate the interactions with individual EHR systems for trial recruitment and follow-up, as well as diagnostic support. The TRANSFoRm project also relies on a workbench to explore clinical and research data repositories. To achieve this, significant challenges need to be overcome in the areas of interoperability and methods for data integration.
Clinical data integration model: GIM instantiation in TRANSFoRm The clinical data integration model (CDIM) is the GIM instantiation in TRANSFoRm, and covers concepts relevant to data integration in primary care research such as medication, diagnosis, and laboratory tests. It is implemented as an ontology web language ontology based on the BFO 1. 1. 56 It imports the general medical science,  58  the vital sign ontology  59  and the information artifact ontology. 60 The ontology also integrates concepts from existing ontologies such as the ontology for biomedical investigations,  61  the gene ontology  62  and the translational medicine ontology  63  when possible. The resulting ontology has 457 classes (102 unique to CDIM) and 73 object properties (1 sub-property unique to CDIM). Twenty-one novel CDIM classes had to be introduced to represent and manage temporal aspects necessary in TRANSFoRm.
Instantiation of structural models, terminologies and mappings Two clinical data repositories were used to evaluate the suitability of the framework for the project: NPCD from the Netherlands and GPRD from the UK. Both their structures and the terminologies used to code information are different. For example, medication is coded with the British national formulary (BNF) codes in GPRD but the ATC classification is used in NPCD, with diagnoses coded with read codes V. 2 in GPRD and ICPC V. 1 in NPCD. Structural models in XML were created for both sources using a semi-automated tool and then loaded into LexEVS. The NPCD database extract we used contained 60 521 anonymized patient records, whereas the GPRD extract made available for the project contained 5000 patient entries. Eight tables (181 fields) in NPCD and 10 tables (107 fields) in GPRD were considered in the structural models.
CDIM was mapped with 44 elements in NPCD and 47 in GPRD. High level classes such as 'processual entity' are part of CDIM and are essential to knowledge modeling but are not expected to be used as mapping targets as they are too generic. Twenty-nine mappings (32%) were one-to-one direct relations between CDIM concepts and a data source structural element. The other mappings included concatenation operations and conditional mappings (including related tables). No virtual elements were necessary for the current data source mappings. Figure  5  illustrates an example of a conditional mapping. Precise and comprehensive knowledge of each data source and its real-life usage was essential to achieve satisfactory mappings and query results. Not all fields of the data sources are targets for mappings, nor are all concepts in CDIM mapped to each data source; their coverage typically differs from CDIM.
Nevertheless, all the relevant entities for the use cases were successfully mapped. Figure  5  presents those mappings necessary to illustrate the examples in figure  6. Figure  6  Examples of query resolution as applied to TRANSFoRm using clinical data integration model (figure  4 ), its mappings to the data sources models (figure  5 ) and terminologies. Highlighted segments represent each level-specific addition based on information from the models served by LexEVS. Based on our use case and available data sources, we focused on ICD-9 and ICD-10 codes, International Classification of Primary Care (ICPC) V. 1 codes, read codes V. 2 for diagnoses, the ATC, the BNF for drugs, as well as on logical observation identifiers names and codes for laboratory tests.
The first example illustrates how to create the local source query based on information contained within CDIM and the DSM. The latter would contain field and table relations required to derive the SQL statement. By utilizing the mappings shown in figure  5 , the query is translated in the local source query format. Similar principles can be applied for multiple sources, but as shown in the first example of figure  6 , the resulting dataset structure is based on the local source. In the example, it is not clear that 'DIAGNOSE' and 'medcode' carry a similar meaning, especially as this equivalence is only true if a condition on the field 'constype' is applied. By adjusting the local query to maintain a reference to CDIM, the resulting datasets from two data sources (NIVEL and GPRD) can be assembled in a coherent structure as in example 2.
Although both result sets now share an identical structure, the terminologies used to code the information are different. In some situations, alignment might not even be possible, at least not in a completely automated fashion as with ATC and BNF for medication types. In this diabetes example, we consider the 'coded with' properties in the local DSM, as previously described. For GPRD, 'Non-insulin dependent diabetes mellitus' in read codes V. 2 can be related to an ICD-10 code (E11) by following mappings in LexEVS. The same can be done for NIVEL with ICPC-1 code T90. 02 to ICD-10 code E11. The final unified dataset is homogenous and consistent semantically as in example 3.
DISCUSSION Achieving interoperability between health data sources such as EHR and registries is a challenging but crucial endeavor for both designers and users of health information technology. The structural and terminological aspects of data source interoperability, while intrinsically linked, have traditionally been handled separately. 65 66 From a structural perspective, a number of projects have adopted a common model to which each source is expected to comply, whether when inputting data (eg, CDASH in the clinical research domain)  15  or when data are being extracted (eg, EU-ADR focusing on adverse event analysis). 22 ther projects have opted for a mediation approach, with a centralized knowledge model, often represented as an ontology. XML and UML designs are also possibilities, as utilized in the BIRN and FURTHeR projects, respectively.
Our framework is built around GIM as the central knowledge model, expressed as an ontology with a realist approach based on BFO 1. 1. The semantic challenges are addressed either through dedicated project-specific tools or through terminological servers, such as the one used in the ePCRN project. The GIM framework is novel in that it uses a terminological server not only for handling semantic interoperability, but for structural aspects as well. Binding both terminological and structural aspects, when they are managed separately, is a challenge that has previously been handled through the use of metadata registries such as caDSR, as used in the caBIG and eMERGE projects. 30 67 The registries allow data elements to be created in which a definition and a list of permissible values is attached.
Our framework avoids this situation by handling the binding in the mediation structure, in which both sets of models are located already. It allows data elements present in existing data sources to be described and integrated readily in the context of GIM and allows the use of local code value sets easily as they are stored in the framework. Our approach represents a step beyond the traditional interoperability paradigm involving a different set of tools for dealing with structural, terminological and binding challenges, in that we present a unified framework that provides an integration solution for these facets inside a single structure. Our LexEVS implementation of GIM, as demonstrated in the TRANSFoRm project, allows a query to be expressed using clinical concepts from a single generic model that is represented as an ontology, and allows its translation into source-specific queries, which then return the results from each source, simplifying and standardizing the interoperability task.
Strengths and limitations One of the biggest barriers to the usage of federated data sources is the resource and effort expected from the data sources to participate in a collaborative structure. 3 In order to mend heterogeneity between two data sources, related elements must be mapped to each other. Whether structural models, such as database schemas, or terminologies are to be aligned, the processes share a common subset of requirements. 68 Multiple approaches have been developed to address the issue. 57 69 Our infrastructure does not necessitate a priori substantial changes to the structure of the data source. If desired, ETL may be used to transform the initial data schema into a derived schema closer to GIM, and this could facilitate the use of direct mappings. If an organization already has a data warehouse, it might be used as is, thereby reducing integration effort and avoiding data duplication.
The architecture presented decouples the interoperability modeling aspects from the application itself. For some data sources, especially EHR, exposing the structure of their databases might not be possible or desirable. In this case, an instance of LexEVS can be installed on a local server, allowing query translation to happen at the local level. From the maintenance perspective, the addition of a new piece of information to a source will necessitate mappings to the relevant GIM terms before becoming usable. 9 Note that our approach can leverage the GIM semantic richness to make this mapping step easier. 70 This occurred with the CDIM implementation of GIM in the TRANSFoRm project, in which we use 'codedWith' properties to suggest concepts that might share similar semantics. Similarly, distance between concepts in the graph can be used to suggest related concepts.
Mappings within TRANSFoRm are currently created manually but should it be expanded, mapping tools will be required in order to support its development. Our LexEVS implementation supports most attributes necessary to allow such work. 70 This has recently been identified as a core challenge to the field by Shvaiko and Euzenat,  68  and we believe that our approach can contribute to an alignment infrastructure, fostering collaboration. There are a number of advantages to using LexEVS as the implementation technology. The GIM ontology is stored in the LexEVS terminology server, allowing us to leverage its two optimization axioms: 'fully restrict then query' and 'lazy loads'. The former minimizes resource requirements by allowing the system to restrict any query fully, including operations on sets (eg, intersections, unions or differences) before running it against the data source.
The latter technique preferentially loads only certain types of information in the first pass while retaining a pointer to load more information dynamically should this be needed. Together, these facilitate efficient query mediation on heterogeneous data sources. Our approach also benefits directly from LexEVS capabilities for handling versioning. Multiple versions of the models, terminologies and mappings can coexist in the system, and be maintained independently from our framework, removing the need for a separate implementation of versioning. Similarly, multilingual capabilities supported by LexEVS can be used for many operations without resorting to an ancillary tool. Once loaded and functional, the framework can leverage intrinsic capabilities of LexEVS to create value sets (ie, subsets of related concepts), which can then be used to handle terminological needs (eg, codes used to represent drugs to treat diabetes) and manage GIM concept groups.
Applicability The infrastructure is currently being deployed in the pan-European TRANSFoRm project, with a view to deploying it in other EU and US translational research projects in academia and industry. Specific TRANSFoRm activities that require combined semantic and structural integration include: ▸ Support for dynamic and persistent linkage between data sources for widely scalable epidemiological studies. ▸ Support for clinical decision support embedded in the EHR, enabling capture and recording of clinical diagnostic cues in a controlled form. ▸ Support for real time linkage to a variety of different EHR systems for extraction of clinical data elements into an electronic case report form and write-back of controlled data elements to the EHR to serve as an eSource for regulated clinical trials.
Deploying CDIM as a unified framework in this setting allows the project tools to have full control over the content and structure of queries sent to data sources, and demonstrated its applicability to multiple deployment scenarios, including distributed installations. This study showed that this unified framework, supported by LexEVS, is a suitable platform in which to achieve these tasks in the context of two exemplar databases. The tool chosen in TRANSFoRm was LexEVS. Nevertheless, in a different context, other tools such as Bioportal might also have the potential to support the framework.
CONCLUSION In this paper, we presented a novel, unifying approach to address interoperability challenges in heterogeneous data sources, by representing structural and semantic models in a single framework. This represents a significant departure from the previous strategies for addressing interoperability in translational research, and it has been successfully demonstrated within the context of the clinical research studies of the EU TRANSFoRm project. The advantage of this approach is that the systems using the architecture can rely solely on GIM concepts, abstracting over both the structure and coding specificities of the data sources. Information models, terminologies and mappings are all stored in LexEVS and can be accessed using the same methods (implementing the HL7 CTS 2 SFM). The system is flexible, and should reduce the integration effort required from the data sources, thereby lowering the cost of entry of this type of research for smaller institutions, and removing the need for larger institutions to invest in additional data warehousing.
Acknowledgements The authors would like to thank their colleagues from the  TRANSFoRm  project for their support and insightful discussions regarding this endeavor. In particular,  Peter Leysen ,  Hilde Bastiaens , and  Anna Nixon Andreasson  developed the use cases. Nasra Khan  provided data extracts and processing at  NIVEL , and  Nick Wilson  at  CPRD. Jean-Karl Soler  assisted in terminology mappings. Lei Zhao  developed terminology services in TRANSFoRm. The authors also thank the members of the  LexEVS development team  for their invaluable help. Contributors JFE created, designed and tested the generic framework, created the ontology and drafted the manuscript. AB and OD participated in the design of the generic framework and worked on the ontology development. AB, OD, VC and BCD helped to draft the manuscript.
There is a 4% change in the predicted cross section when removing all QCD corrections from the diagrams containing light-flavored quark loops. For the b-quark loop  [59] , the QCD corrections are much smaller than for topquark loops, confirming that the procedure does not introduce significant uncertainties. We therefore do not consider any additional uncertainties from this source. For WH and ZH production we use cross sections computed at NNLO  [75]. This calculation starts with the NLO calculation of V2HV  [76]  and includes NNLO QCD contributions  [77] , as well as one-loop electroweak corrections  [78]. For VBF production, we use the VBF cross section computed at NNLO in QCD  [79]. Electroweak corrections to the VBF production cross section, computed with the HAWK program  [78] , are included although they are very small ( 0:03 fb) for the M H range that we consider.
All analyses include uncertainties associated with measurement and acceptances of leptons, which range from 1% to 9% depending on the final state. The largest contribution to all analyses is from the uncertainty on the simulated background cross sections which are 4%-30% depending on the specific background process. These values include both the uncertainty on the theoretical cross section calculations and the uncertainties on the higherorder correction factors. The uncertainty on the expected multijet background in each channel is dominated by the statistics of the data sample from which it is estimated. It is considered separately from the uncertainties on the simulated backgrounds' cross sections and ranges from 10% to 30%. All analyses take into account the uncertainties on the differential cross sections arising from the choice of PDF set and QCD scale.
The H ! W þ W À ! ' þ ' À (' ¼ e, ) analyses divide the data according to jet multiplicity and consider uncertainties on the contribution from GGF that are a function of jet multiplicity. In addition, several analyses incorporate uncertainties that alter differential distributions and kinematics of the dominant backgrounds in the analyses. These uncertainties are estimated from the variation of the final discriminant distribution due to generator and background modeling uncertainties. Correlations between systematic sources are also carried through in the calculations. For example, the uncertainty on the integrated luminosity is taken to be fully correlated between all signals and backgrounds obtained from simulation. Hence any fluctuation in luminosity is common to all channels for a single pseudoexperiment.
Integrated Expected Signal ! W þ W À ! ' þ ' À analysis uses the WW process as the signal with the WZ and ZZ processes as backgrounds. Figure  4(c)  shows the background-subtracted data for the output of the WW discriminant. The VZ analyses measure a WZ þ ZZ production cross section of 0:73 AE 0:32 times the SM prediction of 4. 4 pb obtained with MCFM. The significance for this measurement to be nonzero is 2. 4 s. d. with an expected significance of 3. 4 s. d. The WW production cross section is measured to be 1:02 AE 0:06 times the SM prediction of 11. 3 pb, also based on MCFM. Both measurements confirm our ability to extract a small signal from a large background in the same final states, using the same analysis techniques as the search for the Higgs boson, providing validation of the background modeling.
ACKNOWLEDGMENTS We thank the staffs at  Fermilab  and collaborating institutions, and acknowledge support from the  DOE  and  NSF (USA) ;  CEA  and  CNRS/IN2P3  (France); MON,  NRC KI  and  RFBR (Russia) ;  CNPq ,  FAPERJ ,  FAPESP  and  FUNDUNESP (Brazil) ;  DAE  and  DST (India) ;  Colciencias (Colombia) ;  CONACyT (Mexico) ;  NRF (Korea) ;  FOM  (The Netherlands);  STFC  and the  Royal Society  ( United Kingdom ); MSMT and  GACR (Czech Republic) ;  BMBF  and  DFG (Germany) ;  SFI (Ireland) ; The  Swedish Research Council (Sweden) ; and  CAS  and  CNSF (China).
All screening and treatment were provided free of charge. The data were sourced from TB treatment cards, TB registers and TB-DM registers set up for the purpose of the pilot study. Data were extracted from the registers into paper-based forms and double entered into Epi-Data version 3. 1 (EpiData Association, Odense, Denmark, http://www. epidata. dk). Patients were grouped according to whether or not they had DM, and categorical variables such as baseline characteristics and treatment outcomes were compared using the χ² test, with odds ratios and 95% confi dence intervals as appropriate, with levels of signifi cance set at 5%. Ethics approval was obtained from the institutional ethics committee of Maulana Azad Medical College, New Delhi, and the International Union Against Tuberculosis and Lung Disease Ethics Advisory Group, Paris, France.
Statistical analysis Two-sided P-values were calculated by Chi-square or Fisher exact tests and placed on 2 9 2 contingency tables; Cochran-Armitage test for trend was used for testing trend binomial age proportions across levels of cervical intraepithelial neoplasia. All P-values \ 0. 05 were considered statistically significant. Calculations were performed using SAS software (SAS Institute Inc. , Cary, NC).
Brine generation and assimilation mechanisms Seafloor hydrothermal vents commonly expel seawater-derived fluids with temperatures of ~250-420 °C and salinities ranging from ~0. 1 to 8 wt. % salts (e. g. Campbell and Edmond, 1989; Coumou et al. , 2009; Fontaine et al. , 2007; You et al. , 1994) ; however, fluid inclusions with much higher salinities of 30-50 wt % salts are common in deeper parts of the hydrothermal system (e. g. Kelley et al. , 1992; 1993; Lécuyer et al. , 1999; Nehlig, 1991; Vanko, 1988; Vanko et al. 2004 ). The available data suggest a portion of these brines is sometimes assimilated by deep seated magmas intruding layers 2b and 3 of the crust (Figs 6 to 8; sections 4. 3-4. 4). In this section, we briefly outline how crustal brines with high Br/Cl ratios (Fig  6 ) might be generated and why the assimilated brines have a very limited range of salinity (e.
Acknowledgements Stanislav Szczepanski  is thanked for technical assistance in the  University of Melbourne noble gas laboratory. Dr Mark Kendrick  was the recipient of an  Australian Research Council   QEII Fellowship  (project number  DP 0879451 ). Some of the samples analysed here were provided to RJA by  Charles Langmuir  and  Michael Perfit , 35 years ago, some came from CRPG core shed, others were collected during the  SS07/07 voyage of Australia's Marine National Facility (RV Southern Surveyor ): the Facility's staff, captain and crew, are thanked for their efficient operation of that voyage. I am indebted to  Dr John Bennett  and  Attila Stopic  ( ANSTO ) for undertaking neutron activation analyses and answering numerous queries about neutron fluences and K0 standardisation (supplementary information).