dataesr/scientific-paragraphs-categorization huggingface dataset:
In the 20 patients for whom mutations had been identified by Sanger targeted sequencing prior to the present study, according to the electroclinical presentation (Table  2 ), we did not find any additional potentially pathogenic variant in other genes. Among the 4 patients with previously unknown diagnosis, a missense mutation (c. 845T NG/p. Leu282Arg) of CHRNB2 was identified in patient 4, by both SeqCap EZ and Haloplex library building techniques. Sanger sequencing for the patient and his parents showed that the mutation had occurred de novo. In silico prediction tools (SIFT, Polyphen-2, and Mutation Taster) were in favor of a deleterious effect. It was absent from the ExAC databases of control individuals. Patient 4 had frequent frontal seizures with sleep predominance, consistent with Nocturnal Frontal Lobe Epilepsy (OMIM #600513).
Discussion In this paper, we compared two library-building techniques to develop an NGS gene panel for molecular diagnosis of common epilepsies in a clinical setting. To our knowledge, this is the first study comparing two different technologies for a diagnostic gene panel for epileptic disorders. Only rare studies have compared commercially available kits (e. g. Bodi et al. , 2013). These studies compared technical characteristics but did not compare the efficiency in a diagnostic setting. The full process was first performed on a blind basis, with no a priori about the genes involved and the positions and types of the mutations. To facilitate coverage analysis, we developed a user-friendly software that we named DeCovA. DeCovA displays coverage analysis and depth parameters for each gene of the panel, on a figure.
It can be used individually and inserted in the patient report, to provide information about quality and technical limitations (coverage and depth) that are important for an accredited diagnostic laboratory (Fig. 2 ). In addition, DeCovA can also be used for group analysis to provide information about the properties and limitations of the panel during the technical validation phase (Fig. 3 ). In the present study, the two library-building techniques, Haloplex and SeqCap EZ, showed globally similar coverages (Figs. 2, 3 ), despite a globally more homogeneous coverage for SeqCap EZ. We next compared the variant analysis between the two librarybuilding technologies, using two different software tools: NextGene and Ion Reporter. The analysis process was performed on a blind basis. After this phase, we lifted anonymity and compared data obtained by the blind analysis and the data previously obtained by Sanger sequencing and MLPA.
After this step, we perform sky subtraction using in-house codes, as the very crowded nature of our field requires additional processing to avoid large negative residuals, as shown in Figure  1. In this work, we further improve on the method adopted in paper I, so as to maximise the quality of the sky subtraction across the entire wavelength range of the data cube. In particular, because a significant fraction of our field is filled by the emission lines from the tail of ESO137-001 or by the continuum emission from the stellar disc and halo, we use exposures of empty sky regions to avoid the inclusion of spurious signal arising from the galaxy itself in the sky Figure  1. Sky residuals recorded in a 10-pixel square aperture within one science exposure that has been processed with different algorithm for sky subtraction.
EMISSION LINE MEASUREMENTS To characterise the physical properties of the gas in the disc and the tail, we extract flux maps of the emission lines listed in Table 1. In this table, we also provide for each line an estimate of the noise, quantified as the 3σ limit per spaxel in the unsmoothed MUSE data cube. As in paper I, we assume a systemic redshift zsys = 0. 01555 ± 0. 00015 for ESO137-001. Maps of the line fluxes are obtained from the reduced datacube using the IDL custom code KUBEVIZ. Before the fit, the datacube is median filtered in the spatial direction with a kernel of 15 × 15 pixels (corresponding to 3 ′′ or 0. 95 kpc at the distance of ESO137-001) to increase the signal-to-noise (S/N ) per pixel without compromising the spatial resolution of the data. No spectral smoothing is performed.
In the galaxy disc, Balmer emission lines (Hα and Hβ) are contaminated by stellar absorption. Direct fitting of the emission lines would thus underestimate the fluxes. To solve this problem, we first model and subtract the underlying galaxy stellar continuum using the GANDALF code  (Sarzi et al. 2006)  for spaxels in a rectangular area that covers the stellar disc (see figure  5  in paper I) and with S/N> 5 per velocity channel in the continuum. GAN-DALF works in combination with the Penalized Pixel-Fitting code  (Cappellari & Emsellem 2004)  to simultaneously model the stellar continuum and the emission lines in individual spaxels. The stellar continuum is modelled with a superposition of stellar templates convolved by the stellar line-of-sight velocity distribution, whereas the gas emission and kinematics are derived assuming a Gaussian line profile.
EMISSION LINE DIAGNOSTICS Several physical processes, including photoionisation, shocks, and thermal energy in mixing layers, are likely to contribute to the emission lines we see in the spectra of the main body of ESO137-001 and its tail. In this work we primarily focus on the photoionisation and shocks. In a forthcoming paper, new observations (095. A-0512(A)) covering the full extent of the primary and the secondary tails will be used in combination with multiwavelength data to study the interaction between the warm ionized gas in the tail and the hot ICM.
Areas not covered by MUSE observations are shaded in grey. the tail, which we have identified as HII regions; 4) the diffuse gas within the primary tail; 5) the Hα "blob" in the outer tail. The locations of these different regions are shown in Figure  4 , while Figure  5  shows the composite spectra obtained by co-adding all spaxels in the selected apertures with a mean. Before generating the composite spectra, we remove the velocity offsets due to the systemic redshift differences that arise because of the galaxy rotation. For these corrections we rely on the gas velocity field derived on the new reduction with the methods described in paper I. For the regions 1 and 2, we further use the GANDALF code to remove the stellar continuum spectrum from the composite spectrum, as described above.
BPT diagnostics In section 4. 1, we have shown two dimensional maps of various line ratios. We now use the higher S/N composite spectra to study emission line ratios from the detections of Hβ, [OIII], [OI], [NII] and Hα in the 5 characteristic regions of ESO137-001, defined in Figure  4. Figure  7  shows the BPT diagrams for these composite spectra, using the same colour coding as in Figure  5. The 30 individual HII regions with detections in all the line ratios are also shown as small grey points. The error bars for the HII regions reflect the sample variance, while errors on the mean line ratios are of the size of the datapoint. The grey contours are obtained from the nuclear spectra of a random sample of SDSS galaxies selected from the DR12  (Alam et al. 2015)  database at 0. 03 < z < 0.
ACKNOWLEDGMENTS It is a pleasure to thank  Giulia Mantovani ,  Eva Wuyts ,  Trevor Mendel ,  Mark Krumholz ,  Audrey Galametz  and  Gary Ferland  for useful discussion. We thank the anonymous referee for his/her constructive comments. This work is based on observations made with ESO telescopes at the  La Silla Paranal Observatory  under programmes ID  60. A-9349 (A) and ID  60. A-9303 (A). M. Fossati  and DJW acknowledge the support of the  Deutsche Forschungsgemeinschaft via  Project  ID 3871/1-1. M. Fumagalli acknowledges support by the  Science and Technology Facilities Council  [grant number  ST/L00075X/1 ]. For access to the data used in this paper, the KUBEVIZ line fitting software, and the  IDL  codes to process the MUSE data cubes, please contact the authors.
Therefore, only highly experienced developers are able to address these challenges. Moreover, these developments require an extra effort (in terms of time and cost) and push developers to face unconventional risks derived from dealing with low-level abstractions used in datasheets device specifications, thereby reducing the success rate of these projects. This paper introduces a proposal to deal with the heterogeneity of software interfaces for wearable devices, by enabling the design of high-level software interfaces that ease the development of these systems. The proposal, called model-driven approach for wearable system development (MDWSD), reduces the complexity and effort of using wearables, regardless of their specific characteristics (e. g. , purpose, API, and supported communication protocols).
MDWSD provides a user interface that allows vendors to create high-level interfaces for their devices. Software designers (or architects), who may want to provide simple and homogeneous abstractions about these devices' APIs, can do the same for reducing the effort and risks of wearable system developments. Thus, developers no longer have to deal with low-level complex interfaces for interacting with a wearable device. The proposed approach considers the use of several components: (1) a metamodel that eases the definition of the device design features and its API; (2) a design tool to be used by architects to model the wearables; (3) a mechanism to generate the device model from the proposed metamodel; (4) a software component, called coordinator, which uses the instances of the metamodel (i.
This is because in some cases the developers should create new source code from scratch. (ii) The use of these solutions requires understanding technical documentation, which is not usually written for regular developers. In order to help address these limitations, this paper supports device development and integration, addressing problems such as the large variety of devices to integrate and the lack of the standardization about how to consume device services. The model-driven approach for wearable systems development (MDWSD) proposed in this paper intends to provide a solution for the integration and data management of wearable in an easy and quick way, providing a high-level interface to developers. Typically, the use of these APIs helps them reduce the development effort, risks, and complexity.
When the coordinator has a correct and understandable value, according to the check error criteria specified in the sensor model, it launches an event in the corresponding pipe with the sensor information in a human-understandable format (e. g. , getHr returns 80 beats per minute and getTemp informs 37. 4 Celsius). The coordinator returns this information to the developer, who handles it in the proper way; for example, he/she shows it on the display, processes it, or stores it in a database.
The design tool supports the definition of wearable models, which represent their features and how to interact with them. The device model generation process is used by the design toolto produce instances of the metamodel (i. e. , each sensor model). The coordinator is the software component that handles sensor models in order to facilitate the integration of new devices and allow the use of these sensors in a seamless and transparent manner. Finally, the modeldriven wearable integration process is the component that allows the MDWSD approach components to be coordinated through a single process. This paper also presents a case study to illustrate the development of a software application that gets several functionalities from a commercial wearable device, by using the MDWSD approach. As a future work, we plan to make this proposal deployable as an open software solution, so that it can be used by any developer community.
Acknowledgments This work has been partially funded by the  Facultad de Educación ,  Economía y Tecnología de Ceuta  under the " Contrato-Programa " of research for the period 2013-2015. This work has also been supported by  Fondecyt (Chile) , Grant no. 1150252. The authors would also like to acknowledge input from  COST Action  AAPELE (IC1303).
The FP7 TRANSFoRm project (http://www. transformproject. eu/) was funded via the Patient Safety Stream of ICT for Health. Efficient research design and knowledge translation are a core underpinning of safe clinical practice. It is not good enough to simply avoid error, defined as care that falls well below the average standard, but clinicians should be seeking optimal care for their patients. The LHS, at its barest essential, is all about promoting optimal care. The TRANSFoRm project aimed to develop and demonstrate methods, models, standards, and a digital infrastructure for three specific components of the LHS: (1) genotype-phenotype epidemiological studies using multiple existing primary care and "biobank" genomic datasets; (2) RCTs with both data and trial processes embedded within the functionality of EHRs and the ability to collect Patient Reported Outcome Measures (PROMs) on demand; (3) decision support for diagnosis, based on clinical prediction rules (best diagnostic evidence) and fully integrated with a demonstrator EHR system.
TRANSFoRm Use Cases Diabetes Use Case. The aim of the Diabetes use case is to enable a distributed query to look for eligible patients and extract data from multiple federated databases. In the pilot study, the query will define patients and data to support analysis of the relationship between well-selected single nucleotide polymorphisms (SNPs) in type 2 diabetic patients and the response to sulfonylurea. GORD Use Case. The aim of the GORD use case is to investigate the effectiveness of on demand versus continuous use of proton pump inhibitors on reflux symptoms, quality of life, and self-rated health in patients with gastrooesophageal reflux disease in primary care. The study will be conducted in five localities (UK: two vendors, Poland, Netherlands, and Crete) and it will aim to recruit, randomise, and follow 700 patients at 40 primary care centres using the clinical trial application.
Diagnosis Use Case. The aim of the diagnosis use case is to provide integrated point-of-care decision support for patients presenting with chest pain, abdominal pain, and shortness of breath. TRANSFoRm aims to produce a highly flexible infrastructure that presents the lowest possible barriers to entry for EHR systems and datasets, but at the same time it makes the maximum use of the existing data standards and methods for managing heterogeneity, both structural and terminological, between data sources. A basic principle of the TRANSFoRm project was to use available standards and models as much as possible and integrate them into the TRANSFoRm infrastructure. It was decided early on in the project that TRANSFoRm would take a model-based approach, using 4 models to capture (1) clinical meaning, (2) research meaning, (3) provenance, and (4) diagnostic meaning.
The resulting Clinical Research Information Model (CRIM), in conjunction with CDIM, enabled a twolevel archetype to be defined for each required data element in the use cases. In order to define case report forms and study designs for the RCT, we used the CDISC ODM and SDM standards, but adding an archetype approach for the description of the data element "payload"  [14]. The intention from the outset with TRANSFoRm was that all models would be published, standards would be reused and adapted as required, the software would reuse the existing open source components, if available, and all TRANSFoRm software components would be made available as open source tools under an "Apache" license. We believe that the value lies in the data and the knowledge generated from it and that amortizing the infrastructure can only act as a potential barrier for realising the value of the data/knowledge.
Evaluation of TRANSFoRm will consist of a technical validation of the TRANSFoRm tools and three clinical and sociotechnical evaluation studies. For the DSS, an evaluation of the system, integrated with the In Practice Systems Vision 3 EHR system, is underway. General practitioners are conducting a simulated clinical session with actors simulating patients presenting with carefully prepared test problems. This is a within-subjects design, with the cases solved first without and then with the DSS and the primary outcome being accuracy. We also measure usability and amount of information coded into the EHR. The Diabetes use case is being evaluated on the basis of performance, as judged by users, of the system in selecting and extracting data from five databases. Accuracy of selecting eligible patients by users employing the TRANSFoRm Query Workbench will be measured.
The GORD (gastrooesophageal reflux disease, a disorder caused by the retrograde flow of gastric contents from the stomach into the oesophagus, causing symptoms and/or mucosal damage) study is being conducted as a full clinical RCT (individual subjects randomised) with a nested evaluation study. Principal outcomes of the clinical study are symptom profiles and quality of life measured by PROMs (Patient Reported Outcome Measures) collected on smartphones via a dedicated TRANSFoRm mobile data collection app. The sociotechnical evaluation is a nested cluster trial and will compare recruitment rates, completeness of data, and costs of the TRANSFoRm system compared to usual practice, in this case, a simple web form for the clinical measures and paper questionnaires for the PROMs. The results of the three TRANSFoRm evaluation studies will be available in late 2015.
Results The TRANSFoRm software ecosystem is comprised of a set of generic middleware components that provide essential shared functions for the LHS applications built in TRANSFoRm, namely, secure data transport, authentication, semantic mediation, and data provenance (with respect to processing of data within TRANSFoRm). As LHS is characterized by routine production, transformation, and dissemination of data and knowledge, secure channels and reliable authentication are necessary to ensure confidence and buy-in by the data owners. The data itself resides in a vast array of distributed repositories that vary both in structure and in terminology, making data interoperability a key requirement that TRANSFoRm delivers using a semantic mediation approach combined with the standard data connectivity module (data node connector: DNC).
The DNC implements data interoperability, as well as managing workflow processes and data extraction for participating EHRs and data sources, as discussed in the next section. Different flavours of DNC operate in epidemiology and RCT use cases, as the RCT DNC has to support additional requirements of the RCT workflow. Data provenance capture in TRANSFoRm implements traceability, which is necessary both to support trust and transparency and to enable learning and improvement in LHS processes. On top of these shared components, three application specific tools were built to support the use cases: epidemiological study query workbench, clinical trial monitoring tool, and a diagnostic support plugin for EHR systems. The high-level overview of the software components is shown in Figure  1.
Epidemiological Study Application The epidemiological study TRANSFoRm software configuration (Figure  2 ) is used in the genotypic-phenotypic T2D study use case and consists of tools for secure, provenance-enabled design and execution of eligibility queries and data extractions from heterogeneous data sources. Eligibility queries are   formulated by the researcher in the query workbench (QWB) web tool (Figure  3 ) using model-based constructs (Figure  2 , step 1). QWB users enter clinical terms into the system which then presents the user with a list of corresponding concepts from standard terminologies and classifications (Figure  4 ). The researchers are able to use a data quality tool, storing metadata about available practices and data that reside in them, to restrict the search to practices with a high registration percentage of the variables targeted in the study (step 2).
The queries are dispatched to the data sources via the middleware (step 3) to the local data node connector. This is a TRANSFoRm component that sits at the data source and translates the generic CDIM-based query into a local representation using the semantic mediator component (step 4) and subsequently presents that locally interpretable query either to the data source directly or to a human agent for final approval (step 5), before returning the result. Three types of queries are supported: patient counts, flagging patients, and data extraction. Results of count and flag queries are sent back to the query workbench via the middleware (step 6a) and can be viewed by the researcher in the QWB web tool. The patient data extraction result is passed to a safe haven (step 6b), accessible only to the authorised researcher, using the appropriate secure data transport mechanism.
Clinical Trial Application The clinical trial software configuration (Figure  5 ) is used in the GORD use case and consists of components needed for design, deployment, and collection of trial data, backed by provenance and secure authentication framework for researchers. The trial data collection is supported using electronic Case Report Forms (eCRFs) and Patient Reported Outcome Measures (PROMs). The former are filled in via a web browser by the clinician, while the latter are completed by the patients using either web or mobile devices. Also supported is the orchestration of data collection across multiple clinical sites where the trials are taking place. The TRANSFoRm architecture delivers important components of clinical trials: patient eligibility checks and enrolment, prepopulation of eCRF data from EHRs, PROM data collection from patients, and storing of a copy of study data in the EHR.
The key component of the architecture is the TRANSFoRm Study System (TSS) that coordinates study events and data collections, using HTML form templates with bound queries for preloading data from the EHR. The studies, represented using a custom extension of CDISC SDM/ODM standard, are loaded into the TRANSFoRm Study System (step 1). Whenever an interaction is required between the Study System and EHR, for example, eligibility checks or partial filling of eCRF forms form EHR data, a query is fired off to the EHR via the data node connector (step 2). As in the epidemiological study configuration, the DNC acts as a single point of contact of TRANSFoRm components and the local EHR. In addition to translating and sending queries to the EHR (step 3), the DNC acts as a web server that displays eCRF forms for the clinician to fill with study-required information not present in the EHR.
Once completed, the form is submitted to both the study database and the EHR for storage considering requirements for eSource data use in clinical trials (step 4). The message protocol for this interaction is currently undergoing comparison evaluation with the IHE standards  [17]. The PROM data is collected directly from the patients using web or mobile devices (step 5). The software configuration for the GORD study undergoes a formal Computer System Validation (CSV) process including qualifications for installation, operation, and performance to ensure that study system and study process have been Good Clinical Practice-(GCP-) validated prior to being employed in the GORD clinical trial use case. Because of the narrow connection between EHR and study system, part of GCPvalidation is the assurance of data privacy and confidentiality of the personal patient data.