dataesr/scientific-paragraphs-categorization huggingface dataset:
The PharmML language is extremely flexible both in terms of the range of models that can be encoded, and in terms of the analyses that can be set up through the task component. The "Supplementary information -Examples" file provides a few complete, PharmML-coded models illustrating this versatility. However, during its development, PharmML was validated across a much larger variety of examples, which helped ensure that it could be used to describe a large number of models and clinical situations. The use of SBML together with the task language SED-ML  42  was considered during the conception of PharmML, but discarded because of the intended broader scope of models and the focus on workflows. However, as shown in the examples, models coded in SBML can be easily converted and used. PharmML deals with probability distributions through the ProbOnto module, which allows both to describe the variety of statistical models found in the definition of NLME models and to define the covariate distributions characterizing the study populations.
Another strength of ProbOnto is the unified representation of probability distributions, which is consistent with the unified representation of NLME models in terms of probability distributions  6  and makes it very easy to declare noncontinuous responses. As a result, the language can describe the features of an entire clinical trial. Models may be annotated, i. e. , connected either to ontologies under development in the DDMoRe community, including ProbOnto,  26  or to ontologies such as ChEBI. 43 Annotations can then be used in the search engines, extending PharmML to the semantic web. The manual available on the DDMoRe Model Repository (http://repository. ddmore. eu) provides details on the annotation process. PharmML is distributed under the Apache License, Version 2. 0 license (this can be found in each schema file of the exchange format) and is available both on its website (http://www.
pharmml. org/) and on the GitHub repository (https://github. com/pharmml/ pharmml-spec). There are still scenarios that might be difficult to encode in PharmML. One example could be adaptive clinical trials, for instance, when the doses are adapted based on PK or PD outcomes, or when inclusions are stopped after intermediate analyses. However, the flexibility and modularity of the language would make it easy to extend PharmML to such applications in the near future. Tasks could also be extended, for instance, with noncompartmental analyses and related target software settings. PharmML, as part of the DDMoRe interoperability platform, or as a stand-alone exchange format, has the potential to improve the way pharmacometricians and life scientists work today in multiple ways. A model encoded once can be automatically translated into a representation used in any supported target tool.
Therefore, PharmML can facilitate smooth and lossless transmission of models between tools, enable complex workflows based on standardized model definition, and improve reproducibility of research by simplifying report generation and bug tracking. If adopted across the pharmacometric field, it could also improve the interaction with regulatory agencies. As seen in the SBML community,  12  standards stimulate the development of new tools and methods and facilitate the use of existing models, as has happened with many computational models of biological processes in the BioModels database. 14, 15, 44 Thanks to its flexibility, modularity. and broad scope, PharmML may serve the same purpose in the Modeling and Simulation community.
Figure7snippet exemplifying data-model mapping. Left: declaration of the independent variable and abbreviated model definition the main blocks are shown with indication of their content). Right: complete dataset declaration with column mapping (the dataset is shown as a table for better readability). One <ColumnMapping> element is used for each couple model variable (red)correspondent data column (yellow). In certain cases, such as with categorical covariates, the <CategoryMapping> element is required to map the categorical symbols to the respective symbols used in the dataset.
Acknowledgments. The authors thank the many colleagues across the  DDMoRe  project who have contributed to developing, refining, and implementing PharmML. This study received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement # 115156 , resources of which are composed of financial contributions from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution. The  DDMoRe  project is also financially supported by contributions from  Academic  and SME partners. Conflict of Interest. The authors declared no conflict of interest. Author Contributions. R. B. and E. C. contributed equally to this work. R. B. , E. C. , and  M. J. S. designed  and wrote the manuscript, and produced the use cases. G. S. wrote the manuscript and produced the use cases.
Primary data outputs from these initiatives are increasing exponentially, thus challenging scientific and clinical communities to develop workable solutions for optimal analysis, usage and storage of these datasets. Further complexity is introduced by the need to integrate this genomic data with associated clinical information. Previously, on behalf of the Clinical Working Group of the Global Alliance for Genomics and Health (GA4GH) (a coalition of researchers, clinicians, patient advocates and life sciences/ information technology industries dedicated to implementing worldwide data sharing solutions), we have highlighted the data challenges in cancer genomics  [1] , emphasized the currently siloed nature of the clinical, pathological and genomic datasets and proposed a blueprint solution that is predicated on a culture of responsible data sharing  [2].
These issues pose significant challenges for effective data harmonization and sharing. Thus, a detailed assessment of the current global cancer clinical sample sequencing landscape is required to inform and enhance present and future data sharing efforts. Recognizing these information deficits, we performed an international survey of cancer clinical sample sequencing initiatives. This survey was designed to provide an informative snapshot of current activities worldwide and identify potential barriers that may limit data sharing activities, thus informing creation of a global informatics ecosystem that facilitates the sharing of clinical and genomic cancer data at scale.
Statistical analysis Data collected from Google Forms were exported to the R statistical package for analysis. Descriptive statistics were used to summarize survey responses. All analyses were performed using v 2 testing unless otherwise indicated. Likert scales were used to capture the extent of perceived barriers to data sharing (1 ¼ minor barrier, 6 ¼ major barrier). Given that not all questions were mandatory, sample size varied according to the particular question; thus responses have been displayed with the numerator (n) and denominator (N) (largest possible number of available responses). The denominator is reported for each section once, unless it changes.
Building a cancer specific Human Phenotype Ontology (which has been an invaluable asset to the rare diseases community)  [10] , would significantly enhance phenotype-genotype correlations in the study of malignancy. This survey also highlighted that longitudinal outcome/toxicity data are not captured through a standardized approach outside of clinical trials. Facilitating routine access to these data (e. g. through development of a minimum dataset) is necessary, in order to maximize the collective learning that can be achieved by aggregating clinical/genomic data, especially when analysing rare variants. It was encouraging that 75% of initiatives indicated that their protocol included the permission to re-contact patients, emphasizing the importance that clinical cancer sample sequencing initiatives place on the capture of follow-up patient outcome and toxicity data.
Over 70% of initiatives were in favor of sharing clinical and genomic data. However, a more detailed evaluation of both quantitative and qualitative responses revealed a number of barriers that exist and must be addressed. Lack of dedicated funding was perceived as the most significant barrier to data sharing activities. Collection of even a minimum clinical dataset has major human and technical resource requirements, leading to significant costs. Dedicated funding streams that actively promote data sharing should be encouraged. In this regard, the recent launch of the Innovative Medicines Initiative Big Data for Better Outcomes  [11]  incentivizes both the scientific and pharmaceutical communities to work together in large-scale data sharing activities. The second most commonly highlighted perceived barrier was lack of interoperability of bioinformatics pipelines, and we have already highlighted how initiatives/activities such as GDC  [6] , SMCC  [7]  and the GA4GH API  [8]  are addressing this challenge.
Such an approach has already been employed by GA4GH in the BRCA challenge  [15] , which convened BRCA experts from around the world to work together to share BRCA variants publicly, thus allowing expert review of variant interpretations to determine the pathogenicity of an increased number of variants in the BRCA1/BRCA2 genes. This work has resulted in BRCA exchange  [16] , a curated web portal that allows the BRCA community to query the current evidence of any BRCA1/2 variant present in the aggregated dataset. Extending the BRCA Challenge approach to other genes and cancers would allow a more granular understanding of variant relevance, thereby informing clinical actionability. We acknowledge that this study has several limitations. By its nature, it is a snapshot at a particular moment in time, in a rapidly advancing field.
Acknowledgements This international survey is conducted on behalf of the GA4GH Clinical Working Group. We thank all the respondents for their time and efforts to enable completion of this survey project:  Dara L. Aisner  ( University of Colorado );  Fabrice Andre (SAFIR) ;  Wilson Araujo da Silva Junior (University of Sao Paulo) ;  Philippe Bedard  ( IMPACT/ COMPACT, Canada );  Melissa Brammer  (MyPathway);  James D. Brenton  ( University of Cambridge );  Benedikt Brors  (DKFZ);  Fabien Calvo  ( Cancer Core Europe );  Paulo V Campregher  ( Hospital Israelita Albert Einstein );  Mark Caulfield  ( Genomics England );  George Chong  ( Jewish General Hospital );  Wee Joo Chng  ( National University Cancer Institute, Singapore );  Barbara Conley  ( NCI-Match );  Christopher Corless  ( Oregon Health & Science University );  William S.
Funding CLS was supported by  Howard Hughes Medical Institute and US National Cancer Institute  (Grant # CA008748 ); ML was funded by the  Medical Research Council -Cancer Research UK Stratified Medicine in Colorectal Cancer (S:CORT) Programme  grant; LLS was supported by  Cancer Care Ontario Research Chair and Applied Cancer Research Units  Grant; EEV was supported by the  Barcode for Life Foundation  and the  Hartwig Medical Foundation ; DJV was supported by  Barcode for Life. No grant number is applicable.
Characterization of the disorder using HRTEM proved difficult, because germanosilicates are sensitive to radiation damage. Therefore, we have developed a method for structure projection reconstruction from a through-focus series of HRTEM images acquired with a constant step of defocus changes, implemented in the program QFocus [3]. The structure projection reconstruction method is ideal for beam sensitive materials, because it allows fast data collection without the need of manually optimizing the defocus. The contrast of the reconstructed HRTEM image is greatly improved and the image can be directly interpreted in terms of structure projection. Several through-focus series of 12 HRTEM images with a defocus step of 85. 3Å were taken along the b-axis of IM-18, and the structure projections were reconstructed using QFocus (Fig.
Introduction The Mega Ampere Spherical Tokamak (MAST) is a low aspect ratio device (R/a = 0. 85m/0. 65m ~ 1. 3) with a cross-section similar to other medium sized devices. MAST's high-resolution diagnostic capability is complemented by sophisticated numerical modelling to facilitate deeper understanding. Although MAST has not operated since 2013 there has been substantial analysis and modelling performed on data obtained previously. The main aim of the analysis has been to validate models in order to allow extrapolation to future devices, in particular, MAST Upgrade  [1] , which is currently in the final stages of construction and will begin operation in 2017. Particular attention will be given to the areas of scenario development, fast particle physics and plasma exhaust, for which MAST Upgrade has unique capabilities.
In both ramp-up and ramp-down experiments the current diffusion is not well modelled by TRANSP. This inconsistency between simulation and experiment can be seen in Figure  1a-c. Figure  1b  and c  show the resulting value of the safety factor (q) produced from an MSE constrained EFIT obtained at specific times in the discharge compared to the results obtained from the TRANSP simulation at, respectively, the magnetic axis (q 0 ) and the half radius (q 0. 5 ). Despite a good match to measurements at run initialisation, after 50ms the simulated MSE data lies well outside the region bounded by the experimental error bars. The discrepancy between measurements and simulation is particularly pronounced at the half radius during the current ramp and early flat-top. The discrepancy becomes significant at the axis in the latter stages of the I P flat-top where the modelled current diffusion is faster than that observed experimentally.
In order to guide experimental planning for MAST Upgrade the SOLPS code has been benchmarked against MAST discharges. The parameters extracted from this benchmarking have then been used to simulate both conventional and Super-X configurations in MAST-U  [24]. In comparison to MAST, MAST-U will operate with a closed pumped divertor system (see Figure  4 ). MAST-U is designed to investigate a range of divertor topologies that have different strike point position, connection length and flux expansion. The simulations show that the Super-X configuration will detach at lower density (×1/3) or higher power (×4) with respect to the conventional divertor. The new divertor is predicted to significantly reduce the target power load through magnetic geometry and baffling as the tight closure of the divertor region leads to a strong increase in neutral density with associated power losses  [24].
Acknowledgement We acknowledge the contributions of the EUROFusion MST1 team. This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No  633053  and from the  RCUK Energy Programme  [grant number  EP/I501045 ]. To obtain further information on the data and models underlying this paper please contact PublicationsManager@ccfe. ac. uk. The views and opinions expressed herein do not necessarily reflect those of the  European Commission.
REIMS experimental setup The experimental setup for this study was similar to that reported previously  (Balog et al. 2016). A Medimass REIMS source (Medimass, Budapest, Hungary) was mounted orthogonally to the interface of a Xevo G2-XS quadrupole time-of-flight (QTof) mass spectrometer (Waters Corporation. , Wilmslow, UK) which was operated in negative ion and sensitivity mode. Mass spectra data were acquired over the range m/z 200-1200 with a scan time of 0. 5 s. The REIMS source was connected to a monopolar electrosurgical knife (Model PS01-63H, Hangzhou medstar technology Co, Ltd, Jiaxing City, China) through a 3 m long, 1 cm. diameter ultra-flexible tubing (evacuation/vent line). Electrosurgical dissection in all experiments was performed using an Erbe VIO 50C generator (Erbe Medical UK Ltd, Leeds, UK).
REIMS data pre-processing and analysis Principal component analysis (PCA), an unsupervised technique, linear discriminant analysis (LDA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), both supervised techniques, were used to build the qualitative speciation and catch method models within this study. Raw data generated by the mass spectrometer were preprocessed using a prototype software (Waters Research Centre, Budapest, Hungary) that used standard Masslynx pre-processing algorithms (Waters). The recorded scans for each sample were combined to give an average spectrum and thus one spectrum for each sample was used to build the chemometric models. The resulting data were lockmass corrected using LeuEnk (m/z 554. 2615) and normalised (Total Ion Count-TIC) before being exposed to multivariate analysis.
All chemometric models were calculated using the mass region of m/z 600-950, a spectral intensity threshold of 2e 6 counts and a bin width of 0. 5 Da. When using a m/z range for models that included LeuEnk, variations in the lockmass intensity and interferences with the lockmass compound resulted in a degree of irreproducibility/error. PCA was used to reduce the dimensionality of the data prior to LDA analysis using the first 25 PCA components. The prototype software enabled a leave-20%-out cross-validation of the PCA-LDA score plots in which one average spectrum per sample was analysed. A model was calculated using 80% of the samples and data files left out were classified using the training model. This was repeated five times enabling each sample to be left out once from the model building process.
Real time recognition of samples The PCA-LDA models created using the prototype software were exported to a prototype recognition software (Waters Research Centre, Budapest, Hungary) allowing for real-time identification of samples. Raw data files were acquired and ran live though the software providing a near-instantaneous identification, excluding the delay between sampling and appearance of a signal which was ≈ 2 s. A standard deviation of 5σ was used for class assignment. The spectral intensity limit was set at 1e 8 counts thus ensuring that only the cuts were assigned a species classification and not any background noise.
DNA analysis setup and analysis Mitochondrial cytochrome c oxidase subunit I gene (COI) was used as genetic marker for the examination of samples. DNA extraction was performed using a commercial kit (NucleoSpin Tissue-Macherey Nagel) according to the manufacturer guidelines. A fragment of approximately 655 bp of COI was amplified using the primer pair COIfish_ F1 (5′-TCAACYAATCAYAAA GAT ATYGGCAC-3′) and COIfish_R1 (5′-ACTTCYGGGTGRCCRAARAATCA-3′) in a PCR reaction  (Ward et al. 2005). The sequences were determined by direct DNA sequencing on both strands of the PCR products by BigDye Terminator v3. 1 cycle sequencing kit using the amplification primer pair and analysed on ABI Prism 3130 Genetic Analyzer (Applied Biosystems). Sequences were compared with those deposited in GenBank and Barcode of Life Data Systems (BOLD).
REIMS fish speciation Raw spectrometric data (Supplementary Information S1) obtained from authenticated samples of cod (n = 194), coley (n = 51), haddock (n = 133), pollock (n = 50) and whiting (n = 50) were pre-processed and subjected to multivariate analysis where PCA, LDA and OPLS-DA were applied. 80 PCA components and 4 LDA components were used to generate the chemometric models. Clustering was identified within the three-dimensional (3-D) PCA score plot using components 1, 2 and 4 (Fig. 1a ). However, clear separation between the five species of fish was obtained within the 3-D LDA score plot using components 1,2 and 4 (Fig. 1b ) and the OPLS-DA score plot where 4 latent and 4 orthogonal components were used (Fig. 1c ). A leave-20%-out cross-validation of the PCA-LDA models, where one average spectrum per sample was used resulted in a 99.
37% correct classification (Supplementary Information S2) which was due to two samples being assigned an outlier classification and one whiting being identified as coley. Additionally, a correct classification rate of 99. 37% was obtained for the OPLS-DA model (Supplementary Information S3) which was due to two cod samples being identified as coley and whiting, and one coley sample being identified as whiting. R 2 and Q 2 values of 0. 829 and 0. 809 indicated that the OPLS-DA model had both a good quality of fit and predictivity towards new data. A large Q 2 value also suggests that the multivariate data points are well clustered with there being very few outliers within the dataset as exemplified in all the chemometric models within Fig. 1. The relevant permutation tests (Supplementary Information S4) were carried out to demonstrate that the models were not over-fitted.
DNA analysis of suspect 'haddock' samples During the investigation and generation of the speciation models it was found that six samples labelled as 'haddock' were clustered within the cod samples in all chemometric models. Additionally, the prototype recognition software identified all six 'haddock' samples as cod in which it took 15/20 min to obtain results for all the samples. As a result, the samples were further analysed using PCR to establish whether they were indeed haddock or whether they had accidentally been mislabelled. Mitochondrial cytochrome c oxidase subunit I gene (COI) was used as genetic marker for the six samples, in which all showed 99% similarity with Gadus morhua species (cod) on both Genbank and BOLD. No significant similarities were observed with Melanogrammus aeglefinus (haddock).
Real time analysis of seabass and seabream samples Raw spectrometric data obtained from authenticated samples of seabass (n = 6) and seabream (n = 8) were simultaneously run live through the prototype recognition software providing a near-instantaneous (≈ 2 s) classification. Of the 14 samples analysed, 13 (92. 86%) were correctly identified as outliers with one sample and c orthogonal partial least squares-discriminant analysis (OPLS-DA) models generated using the prototype software and SIMCA 14. All models were generated using the mass range m/z 600-950 of the fish samples with clear separation of the five fish species of fish; cod (orange), coley (red), haddock (green), pollock (blue) and whiting (black) visible within the 3-D LDA and OPLS-DA models being identified as both an outlier (66%) and coley (34%) sample.
Catch method of haddock Raw spectrometric data obtained from both line caught (n = 35) and trawl caught (n = 65) haddock samples were exposed to multivariate analysis allowing PCA, LDA and OPLS-DA models to be generated. 20 PCA components and 2 LDA components were used to generate the catch method models. Some separation was apparent within the 3-D PCA score plot using components 1, 2 and 3 (Fig. 3a ). However, clear separation was attained in the two-dimensional (2-D) LDA score plot using components 1 and 2 (Fig. 3b ), and the OPLS-DA score plot (Fig. 3c ) in which 1 latent and 3 orthogonal components were used. A leave-20%-out crossvalidation of the PCA-LDA models resulted in a 95. 00% correct classification with three trawl caught and two line caught samples being misidentified (Supplementary Information S6).
Discussion Industries across the food sector want fast and accurate results when undertaking their own quality control (QC) checks. DNA approaches, of which most of the studies in Table  1  have employed, fulfil the criteria of obtaining accurate results, but it comes at the expense of long sample preparations and assay running times. Validation of the chemometric speciation models, in which a 98. 99% correct classification (Table  2 ) was achieved using the prototype recognition software (Fig. 2 ) clearly shows that REIMS can fulfil the principle of real time profiling without sacrificing the quality of results that are obtained. Considering that no sample preparation is required, which is a major pitfall for PCR, it is evident that REIMS and maybe other AMS techniques  (Porcari et al. 2012 ) have a prominent role to play in tackling fish fraud.