dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions By including theoretical models for transmembrane proteins of unsolved structures, in addition to atomistic and coarse-grained representations of experimentally solved structures, TMPL fills some of the gaps left by the insufficiently numerous databases of its kind. As a unique feature, TMPL enables users to enrich the database with their own membrane assignments of native or predicted structures, through its easy-to-use web interface and its connection with our OREMPRO web server. Thus, TMPL will centralize our current and future tools for studying transmembrane proteins, as well as our research on high-throughput structural modeling with ORION. This work places itself within the global efforts in structural genomics, focusing on the medically and industrially important transmembrane proteins.
The Brain Images of Normal Subjects (BRAINS) Imagebank (http://www. brainsimagebank. ac. uk) is an integrated repository project hosted by the University of Edinburgh and sponsored by the Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) collaborators. BRAINS provide sharing and archiving of detailed normal human brain imaging and relevant phenotypic data already collected in studies of healthy volunteers across the life-course. It particularly focusses on the extremes of age (currently older age, and in future perinatal) where variability is largest, and which are under-represented in existing databanks. BRAINS is a living imagebank where new data will be added when available. Currently BRAINS contains data from 808 healthy volunteers, from 15 to 81 years of age, from 7 projects in 3 centres.
Introduction What is our imagebank designed to do? The Brain Images of Normal Subjects (BRAINS) Imagebank (http:// www. brainsimagebank. ac. uk) is designed to provide detailed brain imaging data of healthy/'normal' individuals across the human life-course. The imagebank is a searchable database of integrated data sets already collected as part of research studies which include healthy (or control) subjects. These studies include detailed MRI using a range of sequences, and associated data, e. g. demographic details, health, and cognitive tests, and pregnancy details (for perinatal subjects). A key strength of BRAINS is the range and detail of these non-imaging variables. We are initially focussing on collating data from imaging studies at the extremes of life  (old age, and later, perinatal)  where least data are available in other databanks and there is most variability in brain structure  (Serag et al.
brainsimagebank. ac. uk/steering). The BRAINS datasets are from previously funded and ongoing studies by members of the Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE), namely the universities of Aberdeen, Glasgow, Dundee, St Andrews, Stirling, and Edinburgh. It is funded by the Brain Research Imaging Centre at Edinburgh University, SINAPSE (http://www. sinapse. ac. uk/), BBSRC Sparking impact (http://www. bbsrc. ac. uk/), and Edinburgh & Lothians Health Foundation (ELHF, http://www. elhf. co. uk/).
Background: comparison with existing brain image databanks In our systematic review of existing structural brain image databanks, including OASIS, ADNI, UK Biobank, and IXI among others, we found that normal subjects older than 60 years were relatively underrepresented and that these subjects often had limited cognitive and medical metadata to support their classification as 'normal'  (Dickie et al. , 2012a, b). These subjects were mainly recruited as controls in studies of dementia, e. g. OASIS and ADNI, and their classification as 'normal' was generally determined through dementia-based assessments, e. g. , clinical dementia rating (CDR) and Mini-Mental Statement Examination (MMSE). The majority of these subjects were aged between 70 and 79 years. Further, there were a limited range of sequences available for many of these subjects, e.
Data quality control Details of the protocol and quality control processes of the contributing projects will be provided where possible (http://www. brainsimagebank. ac. uk/datasets). This varies between studies and centres. Data from regular phantom scanning is available for all scanners (e. g. the Quality Assurance procedures of the Edinburgh Brain Research Imaging Centre http://www. sbirc. ed. ac. uk/research/qualityassurance/ qualityassurance. html). Details of individual studies image analysis and data checking QC are provided in individual linked publications. In addition, all data are checked during import into BRAINS, both image (sequence details, completeness of scan) and demographic data (matching of IDs to images, completeness of variables, variables range and definitions)see 'data cleaning' below.
Exclusion criteria Imaging: All individual studies require scans to be reported by a clinical neuroradiologist, and anyone with a scan which would represent significant pathology (e. g. cystic periventricular leukomalacia, cerebral haemorrhage or large infarct, perinatal stroke, or large tumour such as pituitary adenoma or glioma) would be excluded, but the presence of incidental abnormalities, e. g. white matter lesions, small infarcts or microhaemorrhages, that are common accompaniments of ageing and which were not associated with any clinically evident impairment, would be included. These features are reported in the database. Clinical: All subjects are identified as 'healthy' or 'normal' when recruited for the original studies. References to the original studies are included in the data provenance, but in general people are living independently in the community, without severe physical, psychiatric or neurodegenerative disease that impacts on their daily life.
BRAINS imagebank architecture The BRAINS imagebank is created using two support databases (Fig. 1 ). The first database, the 'link' database, holds a linking table to enable a secure ID mapping service, for linking of new data sets to existing sets (e. g. identifying subjects with multiple visits), and for reverse linking anonymised data should the need arise. Longitudinal data for a specific subject will have the same subject ID, with a separate visit ID. Initially, the Textual Data pass through 'Data Cleaning' (Fig. 1 ). This involves checking the formats, coding, and ranges of textual data, and converting where necessary to fit the BRAINS schema. The purpose of the second database, the 'Quality Control' (QC) database (MySQL), is to ensure QC for imaging, data linkage, and anonymisation.
It contains data from the DICOM images and medical and related information from the original projects. The database is divided into tables corresponding to two main blocks depending on the origin: DICOM or other. The data are de-identified, but with a relatively low level of anonymity (i. e. name and patient ID removed). The textual data in the DICOM images is anonymised and catalogued using DICOM Confidential  (Rodríguez González et al. , 2010). The link and QC databases are stored on a secure server on our internal network, with strict access restrictions.
Anonymisation protocol The original projects contributing data to the BRAINS imagebank often involve Person Identifiable Data (PID). Data available in the BRAINS imagebank are anonymised, and checked for anonymity. The 'link database' contains a series of tables providing mappings from the original patient IDs and subjects' study IDs to their assigned BRAINS subject ID (BID). The BID is a randomly generated ID following the pattern "BIDxxxxx" where x is a digit. It uses the Java random number generator (48-bit seed, which is modified using a linear congruential formula). We also check for collisions with previously generated BIDs. These BIDs are generated when data for a new subject are processed for their inclusion in the QC database. The mapping between the original ID and the generated BID is stored in the 'link database' in order to ensure that longitudinal data are assigned to the correct BID.
The DICOM images are anonymised in a multi-stage process. Firstly, basic DICOM de-identification is performed for loading the data into the QC database. This removes the most obvious identifiers like the person's name, and patient ID, and removal of all private tags and rejection of DICOM images with information burned into the pixel data. The patient ID is substituted by the BID. This step is performed by DICOM Confidential and includes the generation of the DICOM catalogue tables in the QC database. Further and much stricter DICOM de-identification is performed, again using DICOM Confidential  (Rodríguez González et al. , 2010) , before sending the DICOM images to the BRAINS imagebank. The privacy policy in this case is based on the DICOM standard basic profile with the 'keep longitudinal information' option.
BRAINS schema The BRAINS schema covers the range of imaging and associated data in each included study. Not all studies collect or provide all associated data. To implement the BRAINS schema XNAT data types were enriched and specialised by child data types specified in XML Schema documents. The initial release of BRAINS will be searchable by a core subset of the schema fields. As the BRAINS imagebank is developed we will extend the searchable subset to cover the complete set of schema fields. A data dictionary of the non-imaging data included can be found at http://www. brainsimagebank. ac. uk/datadictionary and a summary of all current data can be found at http://www. brainsimagebank. ac. uk/ subjectQuery/summary. The A major issue in combining these heterogeneous datasets is how to combine Textual data which may be recorded in different ways (e.
g. age as whole years, months, weeks or days, or in decimal format), measured in different ways (e. g. blood pressure using an automated or manual sphygmomanometer) or using different tests to measure the same construct e. g. National Adult Reading Test (NART) or Mill Hill test for vocabulary/crystallised ability. An ontologist working with domain experts creates mappings from the original dataset to the BRAINS schema, and all decisions are recorded. Missing data are coded as 'not available'. Details of how we standardised the format of textual data will be available on our website once standardisations are finalised. The current (human readable) Textual data schema can be found at http://www. brainsimagebank. ac. uk/datadictionary, and (xml format: http://sourceforge. net/projects/ brainsimagebank/).
Governance The BRAINS steering committee is comprised of the BRAINS Imagebank management team, Principal Investigators (PIs) of included studies, legal and ethical experts and lay representatives. BRAINS has ethical approval for anonymous use of data from the West of Scotland Research Ethics Committee (http://www. nhsggc. org. uk/about-us/ professional-support-sites/research-development/west-of-scotlandresearch-ethics-service/), to whom we will provide annual reports of all activity, including a risk register and a record of any serious adverse events.
Data access request and data usage agreement Once the user has identified the data of interest they can submit a project proposal to the BRAINS imagebank with a 'Data Access Request form' (http://www. brainsimagebank. ac. uk/dataRequest). The user's identity will then be validated by establishing a valid link between the user and their organisation. Both imaging and associated data should be requested together, preferably with a saved query. The proposal Occupation/years of formal education/smoking status/alcohol use will be considered by the BRAINS Steering Committee by seeking active agreement by at least 3 members, and no disagreement by any member, with a target for decision within 5 working days. The proposal will be evaluated against the BRAINS ethical agreements. If the proposal is judged to accord with those agreements and represents an appropriate usage of the data according to principles agreed by the Steering Committed, then a "Data Usage Agreement" will be made, and lastly the BRAINS data managers will provide the user with access to the specified data.
Revisions and withdrawals There is a mechanism for informing existing users of data that has been withdrawn, but we do not have the means to support this service at this time. Users will be notified of new or updated data sets in the BRAINS imagebank as they become available. We request that users of data inform us of publications, and acknowledge the use of data from BRAINS. We also request that they supply a list of the actual data used (if not the full set of variables requested) to facilitate replication of results and testing of methods.
Image and Textual data collection What is available? Data currently in the BRAINS image bank: The BRAINS imagebank currently consists of data from 808 healthy volunteers, age range: 15-81 years, spanning seven previously funded projects. This is a living imagebank, with new data added when available. The current summary of included data is at http://www. brainsimagebank. ac. uk/subjectQuery/summary. These pseudo anonymised data sets contain a range of structural MRI sequences, including T1, T2, T2*, FLAIR, in DICOM format, and a range of associated data, e. g. perinatal details and sex (for neonatal data), blood pressure, age, Mini Mental State Examination (MMSE) (in CSV format). The associated data are too extensive to list here: see section on 'BRAINS schema' above. Simpsons Study  (Shenkin et al.
, 2009) : This study contains n = 111 subjects aged 75-81 years with MRI sequences T2, T1, T2*, FLAIR at 1. 5 T and detailed demographic, physical and cognitive measures. The study was funded by the Medical Research Council Clinical Training Fellowship to Susan  Shenkin (www. mrc. ac. uk), and Chest Heart & Stroke Scotland (www. chss. org. uk). Aberdeen Birth Cohort  (Whalley et al. , 2011a) : The study contains n = 244 subjects aged 77-80 years with MRI sequences T2, T1, FLAIR at 1. 0/1. 5 T and detailed demographic, physical and cognitive measures. (http://www. abdn. ac. uk/birth-cohorts/1936/). The study was funded by The Engineering and Physical Sciences Research Council; Pump Priming Grant Scheme (www. rcr. ac. uk/pump-priming-grant-scheme), Tenovus Scotland, SINAPSE, Pfizer, TMVS, Varian, GE healthcare, Siemens, GSK, ReNeuron, LUX Innovate, Propeller, NHS R&D, SHIL, SFC SPIRIT, Alzheimer's Research UK.
Psychological, social and biological determinants of ill health (pSoBid) in Glasgow: A cross-sectional, population-based study. Funded by The Glasgow Centre for Population Health (2013)  (Krishnadas et al. , 2013 and McLean et al. , 2012)  which is a partnership between NHS Greater Glasgow and Clyde, Glasgow City Council and the University of Glasgow, supported by the Scottish Government. The study contains n = 45 subjects aged 20-60 years with MRI sequences T2, T1 at 3. 0 T and detailed demographic, social, physical and cognitive measures. Amygdala 2 study  (Hall et al. , 2007) : This study contains n = 55 control subjects aged 19-60 years with MRI sequences T2, T1 at 1. 5 T and demographic information and cognitive testing. This study was funded by Stanley Medical Research Institute (www2.
stanleyresearch. org), and the Sir Mortimer and Theresa Sackler Foundation (opencharities. org/charities/1128926). NIH DTI study  (Dickie et al. , 2015a, b) : The contributed data contains n = 80 Control subjects aged 25-65 years with MRI sequences T2, T1, T2*, FLAIR (DTI to be added) at 1. 5 T, demographic information, brief medical history and cognitive testing. This study was funded by the National Institutes of Health grant R01 EB004155-03. Bipolar Family Study  (Whalley et al. , 2011b; Sprooten et al. , 2011) : The contributed data contains n = 224 Control subjects aged 15-28 years with MRI sequences T1, T2, DWI at 1. 5 T. Glasgow 45 Healthy Volunteers (http://theses. gla. ac. uk/id/eprint/ 3133): The contributed data contains n = 45 control subjects aged 19-64 years, years with MRI sequences FLAIR, T2, T1 at 3 T and basic demographic details.
Discussion and future work The BRAINS imagebank is a living imagebank, meaning that it will include more data, with a further 1607 existing sets of data planned for immediate inclusion, spanning nineteen projects. Further data from ongoing and future studies with healthy volunteers will be included when it becomes available. For example, an estimated 3000 new healthy volunteers spanning a range of ages from 1st to 7th decades will be included over the next five years from studies in Scotland, giving over 5000 subjects by the year 2020. We will also link with other initiatives, e. g. in the Dementias Platform UK (http://www. mrc. ac. uk/research/ facilities/dementias-platform-uk/) to maximise opportunities to increase sample size in meta-analyses of early dementia risk. Due to the inclusion of heterogenous studies, we provide as much provenance data (http://www.
brainsimagebank. ac. uk/datasets) as possible for each data set, e. g. recruitment methods, exclusion criteria, original study purpose, geographic area, ethics, and consent form. Citable Digital Object Identifiers (DOIs)/Uniform Resource Identifiers (URIs) will be included for each available dataset. Principal Investigators (PIs) who contribute data will complete a data contribution agreement (http://www. brainsimagebank. ac. uk) and provide provenance data including links to original publications describing the study. PIs only provide data that they agree can be shared, e. g. once primary analyses are complete. They are not obligated to provide all variables from their study. Lastly, we will provide processed and analysed data, primarily to support the creation and use of statistical reference models, brain templates and reference atlases (e.
g. ,  Dickie et al. , 2012b Dickie et al. , , 2013 Dickie et al. , , 2014 Dickie et al. , , 2015a Dickie et al. , , 2015b)). Data are provided to BRAINS from the original study Principal Investigators, with federation where possible to allow comparison between similar variables. Data provenance and subsequent data harmonisation is described for all variables (http://www. brainsimagebank. ac. uk/datadictionary). No cross-site standardisation of imaging or non-imaging data was performed, with the exception of CaliBrain  (Gountouna et al. , 2010) , a multicenter harmonisation study. BRAINS represents a post-hoc data repository, to facilitate the use and re-use of previously collected data, which will contribute to increased value and reduced waste in imaging research  (Macleod et al. , 2014).
Acknowledgments Direct funding of BRAINS:  SINAPSE-SPIRIT  funded the main  BRAINS  project. D. Job ,  D. A. Dickie ,  D. Gonzales , and  S. Shenkin  were supported by the  Brain Imaging Research Centre , the  University of Edinburgh, Scotland, UK ; the  BBSRC Sparking Impact  scheme ( BBSRC: -SI 2013-0210 );  Edinburgh & Lothians Health Foundation  (ELHF, reference  53/311 ). A. Robson  was supported by the  Scottish Funding Council (SFC SPRI-NG , and  SFC HR09021 ). Our funding sources had no involvement in this work.
Kinetic analysis To determine kinetic parameters, numerical models, namely "decomposition kinetic models", were developed using a least-squares fitting procedure based on TG and/or DTG curves  [26]. To identify a kinetic decomposition model, TG measurements at various heating rates are performed  [26, 27]. The analyses of the decomposition mechanism permit to give a physical sense to the kinetic analysis. From a technical point of view, a kinetic analysis is also a tool for data reduction. From a series of measurements with many data points, a model with few parameters is extracted. Then, these parameters will be used for predictions of reaction progress for given temperature conditions. The predictions can be done for one or several measurements like heating. Depending on the studied material, the decomposition can be highly complex.
The most used function is based on reaction n th order kinetics following Equ. 2 = - α f( ) (1 α) n ( 2 ) where n is the reaction order. The Thermokinetics3 software (NETZSCH, Selb, Germany) was used to calculate kinetic parameters. This software includes the two different approaches namely model-free and model based kinetic analysis  [11] [12] [13] [14] [15] [16] [17] 29]. A method proposed by Moukhina  [30]  to obtain kinetic parameters is followed here. To summarise, this method consists in creating a kinetic model step by step permitting to select the best model. First, a model-free analysis is performed by using Friedman procedure on thermogravimetric data  [14]. It permits to plot the activation energy as a function of the degree of conversion (decomposition) degree, which exhibits one or more reaction steps.
The Task Force writing the guidelines were instrumental in defining the content of the apps. The guideline flowcharts for different sections of AF management determined the design of the apps. This process began in October 2014, an outline of the app functionalities was completed in October 2015, and the final guideline structure was incorporated into the Apps Software Requirements Specification by February 2016. Model View Presenter Framework (Android) and Model View Controller (iOS) were used, with the overall product based on Client Server Architecture. The apps were publicly released at the ESC Congress in Barcelona (August 2017). As the Healthcare Professional app determines patient treatment, a CE (ConformiteEuropeene) certification application has been submitted to conform with European Commission Directives (Class IIa medical device).
Acknowledgements We would like to thank  Marios Hadjianastasis  for guidance on digital learning ( Centre for Learning and Academic Development, University of Birmingham ) and the  British Heart Foundation  for assistance with the patient education material. Conflict of interest: D. K. reports non-financial support from  Daiichi Sankyo , grants from  Menarini , lecture fees from  AtriCure , all outside the submitted work; and Lead for the Beta-blockers in Heart Failure Collaborative Group (BB-meta-HF) and  RAte control Therapy Evaluation in Atrial Fibrillation (RATE-AF) Trial. L. F. reports grants from  EU Horizon 2020  ( CATCH ME ) during the conduct of the study; grants from  DFG (Deutsche Forschungsgemeinschaft ), grants from  Medical Research Council , grants from  British Heart Foundation , grants from  Gilead , all outside the submitted work; in addition, L.
F. has a patent for Personalised AF Therapy ( GB1404998. 5 ) and Markers for AF (GB1413081. 9)issued to the  University of Birmingham. B. C. reports a grant from  EU Horizon 2020  ( CATCH ME ) during the conduct of the study; grants from  British Heart Foundation  and one un-restricted grant from  Astra-Zeneca , outside the submitted work. U. S. reports grants from the  European Union , during the conduct of the study; personal fees from  Roche Diagnostics , non-financial support from  Bayer Healthcare , grants and non-financial support from  Medtronic , grants from  Netherlands Heart Foundation , grants from  Netherlands Genomics Initiative , all outside the submitted work; in addition, U. S. has a patent for Non-invasive Classification of AF issued. P. K. reports consulting fees, honoraria and research grants from  3M Medica, MEDA Pharma, AstraZeneca, Bayer Healthcare, Biosense Webster ,  Boehringer Ingelheim, Bristol-Myers Squibb, Cardiovascular Therapeutics, Daiichi-Sankyo, German Cardiac Society -Akademie, Johnson & Johnson, Medtronic ,  Merck ,  MSD ,  OMRON ,  Otsuka Pharma ,  Pfizer ,  Sanofi ,  St.
Funding The smartphone and tablet applications have been funded by the  CATCH ME Consortium  ( EU Horizon 2020 Grant 633196 ) and the  European Society of Cardiology. D. K. is supported by a  National Institute of Health Research (NIHR)   Career Development Fellowship  ( CDF-2015-08-074 ). L. F. has received institutional support from  DFG ,  MRC , the  EU , and  Gilead. J. H. is supported by a  Post-Doctoral Fellowship  from the  Heart Foundation of Australia. U. S. is supported by a grant from the  Netherlands Heart Foundation  ( CVON2014-09 , RACE V). B. C. research is supported by the  British Heart Foundation  and the  European Union. The opinions expressed in this paper are those of the authors and do not represent any of these funders.