dataesr/scientific-paragraphs-categorization huggingface dataset:
Companion app The objective of the Companion app is to encompass all other PEGASO apps (Fig. 2 ). The Companion App aims to act as a personal digital friend which coaches, cares, and empowers participants in developing healthy habits. It aims to provide the participant with a dynamic stream of educational and motivational messages and updates about their achievements and their friends' shared achievements. Data acquired via physical activity monitors is presented to the participant via a specific interface presented within the Companion app. The Companion app also includes a reward system and the gamification module to allow the participants to: a) achieve one time rewards (e. g. , FitCoins, badges, experience points) for the selected target behaviour and b) achieve cyclic events.
Each focus group will consist of 4-8 participants. The groups, whenever possible, will be gender-balanced with different levels of engagement to the PEGASO system. A trained moderator will guide the focus group following a script and will be assisted by an observer. All of them will be audio and/or videotaped and transcribed systematically, literally, and anonymously. An analysis will be made of thematic content, coding the data and grouping them into predefined categories based on topics.
Data collected by the PEGASO system The interaction between the user and the Pegaso system is recognized through of a set of automatic engagement indicators measuring the actual usage of the system. For each participant, actions like interacting with the Companion app or registering a meal in the EDiary app are tracked and daily stored. The EDiary app records information about the reported meals composition, food groups and servings. The smartphone collects information about physical activity such as the number of steps or the total time spent doing physical activity. Further information on physical activity is collected from the 4th month by the introduction of a smart bracelet and smart sensor. General information about healthy habits is gathered through the "Self-Report Habit Index (SRHI)" adapted to include items that reflect self-identity  [44].
Analysis plan and statistical analysis The analysis plan will include a description of all data at every assessment point and an overall comparison of baseline characteristics between intervention group (IG) and comparison group (CG), as well as by sites and gender. Knowledge/awareness, lifestyles and anthropometric measurements will be compared at pre and post intervention (month 6) utilising an intention-to-treat (ITT) approach, independently from drop-outs and nonadherence. Relevant changes among lifestyles and knowledge will be defined according to current recommendations from international guidelines  [46]. The effect of the intervention could be modified by gender, cultural and socioeconomic level, body mass index (BMI), as well as by the baseline lifestyles, which will be controlled in the analysis.
The level of statistical significance will be set at α = 0. 05 and all tests will be two-tailed. Statistical analyses will be carried out using the software packages SPSS, version 22. 0 (SPSS Inc. , Chicago, IL) and Stata (Stata-Corp. 2009. Stata Statistical Software: Release 11. College Station, TX: StataCorp LP). Focus groups will analysis will be done with the support of Atlas. Ti /NVIVO and by triangulation of analyst.
Self-reported data is used to measure main results related to healthy habits modification and knowledge. These are assessed through the validated questionnaires selected for this trial. According to the availability of each site, two approaches for the selection of comparative groups were foreseen; the comparative group either came from the same school or from a different school. Both, clustered and non-clustered quasi-experimental studies have their own advantages and disadvantages. A possible crossover effects regarding the fact that both intervention and comparative groups are from the same schools may occur; social peer influence can be strongly associated to lifestyles and influence the results of the comparative group. However, we believe that adolescents tend to mainly interrelate with mates from their same class.
Acknowledgements The authors gratefully acknowledge scientific and technical assistance provided by  Carme Carrion Ribas ,  Alyson Langley ,  Sarah A. Tabozzi  and  Jose Serrano  as well as all partners in the  Pegaso Fit for Future Study. We would also like to thank all the participating schools: students, parents, teachers and school principals for their commitment. Participating schools:  Bluecoat Beechdale Academy, Djanogly City Academy, Bluecoat Beechdale Academy ,  Armadale Academy ,  Lasswade High School ,  Liceo Scientifico Elio Vittorini ,  ITAS Giulio Natta ,,  Escola Nou Patufet ,  IES Ramon de la Torre ,  Escola Fuster ,  Col•legi Manent Rambla ,  FEDAC Santa Coloma and Virgen del Carmen. PEGASO Fit For Future Consortium  (in alphabetical order): AQuAs:  Carme Carrion ,  Mireia Espallargues ,  Santiago Felipe Gomez ,  Elisa Puigdomenech.
Funding This project was funded by research grants of the  European Commission  under the  7th Framework Programme (Call identifier :  FP7-ICT-2013-10 ; Project number:  610727 ). Funding has allowed the evaluation work package of the project to design the study, collect and analyse data and in writing the manuscript. AM was supported by the  UK Medical Research Council  (grant number  MC_UU_12017/ 14 ) and the  Scottish Government Chief Scientist Office  (grant number  SPHSU14 ).
Combining Pan-STARRS and WISE data Before combining with the radio data, the Pan-STARRS i-band and WISE W 1-band data sets were first combined, using a magnitude-only LR analysis. The WISE W 1 was used as the basis data set and the best Pan-STARRS match (if any) to each WISE source was sought. The matching was done in this direction, since both the angular resolution and source density of the Pan-STARRS data are much higher, and so matching in the opposite direction would lead to multiple Pan-STARRS galaxies selecting the same WISE source. The use of WISE data helps the subsequent LR matching to LoTSS sources given that radio sources are frequently associated with galaxies with redder colours and hence brighter near-infrared magnitudes. Although we do not explicitly filter out optical galaxies with no WISE emission, our colour-based LR method is effective at rejecting these when they are unrelated.
The counterparts accepted (those with LR > 6. 4) are broadly similar to those that would be selected by adopting a simple NN radial cross-matching out to ≈ 2 , but with a weak magnitude dependence on the allowable radial offset. A combined Pan-STARRS-WISE catalogue was constructed by including all accepted cross-matches, but also retaining all WISE sources without a Pan-STARRS match, and supplementing the catalogue with all of the Pan-STARRS catalogue sources that had not been matched to a WISE source. For all catalogue entries, the magnitudes were converted into AB magnitudes and corrected for Galactic reddening using the data of  Schlegel et al. (1998). The overall catalogue contains around 26. 5 million entries, of which just over 30% had detections in both bands, nearly 20% were detected only in WISE, and 50% were detected by Pan-STARRS only.
Some issues will undoubtedly remain with the combined catalogue, for example in cases where two nearby Pan-STARRS sources are blended in the lower resolution WISE data into a single catalogue entry; however, these are sufficiently rare that they are not expected to have a significant effect on subsequent LoTSS cross-matching. We note that no attempt was made to separate stars from galaxies in the combined catalogue: LoTSS sources may match to stellar objects (either genuine -such as Pulsars -or misclassified objects such as quasars) and the adopted colour-dependent procedure already works sufficiently well at down-weighting the LRs of stellar candidates that attempting to exclude these would introduce more errors or biases than potential benefit.
Combining LoTSS and Pan-STARRS-WISE data We use the full colour-and magnitude-dependent LR method described in Section 4. 1 to cross-match the LoTSS-DR1 sources with the combined Pan-STARRS-WISE catalogue. Specifically, in the LR analysis we consider the iband magnitude (m) and the i -W 1 colour (c). For the 80% of sources with detections in Pan-STARRS, we use the Pan-STARRS positions, while for the remainder we use the WISE positions. From within the overall LoTSS-DR1 sample, the subset of radio sources for which LR analysis is appropriate was selected. These are ideally the sources for which the PyBDSF radio source position provides a well-defined location for where the radio source host galaxy is expected to be, and not those PyBDSF sources that are parts of a larger source or are very significantly extended and thus have poorly defined positions.
The LGZ interface As in our pilot projects, we made the design decision to carry out in parallel the two processes of 'association' (where the volunteer decides whether several sources in the PyBDSF catalogue should be treated as a single source) and 'identification' (where the volunteer selects zero, one or more optical host galaxies for the possibly associated radio source). In many cases the position of a plausible optical host is very helpful in deciding on the correct source association, or vice versa. We therefore needed to present the volunteer with images to classify that showed the radio data and at least one optical image. After some experimentation, we chose to use both the Pan-STARRS r-band image and WISE band 1, together with radio contours from both the LoTSS images and the FIRST survey.
Acknowledgements This paper is based on data obtained with the  International LOFAR Telescope (ILT)  under project codes  LC2 038  and  LC3 008. LOFAR (van Haarlem et al. 2013) is the LOw Frequency ARray designed and constructed by  ASTRON. It has observing, data processing, and data storage facilities in several countries, which are owned by various parties (each with their own funding sources) and are collectively operated by the ILT foundation under a joint scientific policy. The ILT resources have benefited from the following recent major funding sources:  CNRS-INSU, Observatoire de Paris and Université d'Orléans, France ;  BMBF ,  MIWF-NRW, MPG, Germany ;  Science Foundation Ireland (SFI) ,  Department of Business, Enterprise and Innovation (DBEI) , Ireland; NWO, The Netherlands; The  Science and Technology Facilities Council, UK  [7].
Part of this work was carried out on the Dutch national e-infrastructure with the support of  SURF Cooperative  through grant e-infra 160022 and we gratefully acknowledge support by  N. Danezi  (SURFsara) and  C. Schrijvers  (SURFsara). This research has made use of the  University of Hertfordshire  high-performance computing facility (http:// uhhpc. herts. ac. uk/) and the  LOFAR-UK computing facility  located at the  University of Hertfordshire  and supported by  STFC  [ ST/P000096/1 ]. This research made use of astropy, a communitydeveloped core Python package for astronomy (Astropy Collaboration et al. 2013) hosted at http://www. astropy. org/, of  APLpy (Robitaille & Bressert 2012 ), an opensource astronomical plotting package for Python hosted at http://aplpy. github. com/,and of topcat (Taylor 2005).
The  Pan-STARRS1 Surveys  ( PS1 ) and the PS1 public science archive have been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , the  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No. NNX08AR22G  issued through the  Planetary Science Division of the NASA Science Mission Directorate , the  National Science Foundation  Grant No.
A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws.
The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive posterior distributions of kinetic parameters consistent with the observed physiology. In this work, we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are their values that give rise to a desired metabolic behavior? Such information is important for a wide variety of studies ranging from biotechnology to medicine. To illustrate the proposed methodology, we performed Metabolic Control Analysis, computed the flux control coefficients of the xylose uptake (XTR), and identified parameters that ensure a rate improvement of XTR in a glucose-xylose co-utilizing S. cerevisiae strain. Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology, and ultimately to design and control the desired responses of the metabolism.
We used the extended iSCHRUNK to identify the distribution of kinetic parameters that determine the sign in ambiguous distributions of control coefficients as follows: i. We defined the stoichiometric model of glucose-xylose co-utilizing S. cerevisiae (S4 Fig). The model consisted of 102 atomically balanced reactions and 96 intracellular metabolites, and included glycolysis, pentose phosphate pathway, tricarboxylic cycle (TCA), electron transport chain (ETC) and XR/XDH xylose assimilation pathway  [30, 72]. Based on the physiological information on the cellular compartmentalization the intracellular metabolites were categorized as cytosolic or mitochondrial, and the extracellular metabolites were modeled as well. We integrated the thermodynamic constraints based on the information about the Gibbs free energies of reactions  [73] [74] [75] [76]  together with the fermentation data from Miskovic et al.
At this point of the procedure, we may integrate available Michaelis constants, K m , from the literature and databases  [84, 85]. In this study, we did not use K m values from the literature, instead, we sampled the space of K m values indirectly through the sampling of the degree of saturation of the enzyme active site, σ A  [10]. For a vector of metabolite concentrations computed in Step II, we calculated the K m value corresponding to a value σ A as K m = S j (1σ A )/σ A , where S j is the j th element of the metabolite concentration vector that corresponded to σ A  [10]. Without prior information, we sampled σ A values between 0 (non-saturation) and 1 (full saturation). Otherwise, we performed the stratified sampling where we imposed the σ A distributions obtained from the classification algorithm in Step VII (Fig 7A  and 7B ).
The classification problem was defined to find the ranges of the σ A values (and consequently the ranges of the corresponding K m values) that determine the sign, positive or negative, of the analyzed control coefficient. We solved this parameter classification problem using the CART algorithm  [33, 34]  from the MATLAB software package. We then used the output of the parameter classification, the distributions of σ A , for the sampling in Step IIIb (Fig 7A ). More details about the parameter classification are presented in the next section. vii. In this step, we can postulate hypotheses and design systems biology strategies. In Step V we entered an iterative loop for identifying the ranges of σ A (or equivalently K m ) for which the analyzed control coefficients were well determined (Fig  7 ).
Background. In biology, high-throughput experimental technologies, also referred as ''omics'' technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project. Methods. The Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub (https://github. com/Candihub/pixel). It is written in Python using the Django framework and stores all the data in a PostgreSQL database.
It is developed in the open and licensed under the BSD 3-clause license. The Pixel Web App is also heavily tested with both unit and functional tests, a strong code coverage and continuous integration provided by CircleCI. To ease the development and the deployment of the Pixel Web App, Docker and Docker Compose are used to bundle the application as well as its dependencies. Results. The Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data.
If HTS is often put at the forefront of ''omics'' technologies (essentially genomics and transcriptomics,  Reuter, Spacek & Snyder, 2015) , other technologies must be considered. Mass spectrometry (MS) for instance, enables HT identification and quantification of proteins (proteomics). Metabolomics and lipidomics are other derived applications of MS to characterize quantitative changes in small-molecular weight cellular components  (Smith et al. , 2014). Together, they all account for complementary ''omics area'' with the advantage to quantify distinct levels of cellular components  (transcripts, proteins, metabolites, etc. ). Integration of datasets issued from different HT technologies (termed as multi-omics datasets) represents a challenging task from a statistical and methodological point of view  (Huang, Chaudhary & Garmire, 2017).
It can be the counts of reads per genes for HTS results or the abundance values per proteins for MS results. In multi-omics datasets analysis, combining secondary data is essential to answer specific biological questions. It can be typically, the identification of differentially expressed genes (or proteins) between several cell growth conditions from transcriptomics (or proteomics) datasets, or the identification of cellular functions that are over-represented in a list of genes (or proteins). In that respect, secondary data can be analysed and re-analysed within a multitude of analytical strategies, introducing the idea of data analysis cycle. The researcher is thus constantly facing the challenge to properly annotate, store, explore and mine all the biological data he/she has at his/her disposal in a multi-omics project.
It requires to adopt good practices for reproducible-research on a daily basis, and not only when the final results are about to be published. Initiatives to improve computational reproducibility exists  (Peng, 2011; Stodden, Guo & Ma, 2013; Vasilevsky et al. , 2017; Rougier et al. , 2017; Stodden, Seiler & Ma, 2018) , and today it is clear that the data alone are not enough to sustain scientific claims. Comments, explanations, software source codes and tests are prerequisites to ensure that an original research can be replicated by anyone, anytime, anywhere. We developed the Pixel web application (Pixel Web App) with these ideas in mind. It is a content management platform to help the researchers involved in a multi-omics biological project, to collaboratively work with their HT data. The Pixel Web App does not store the primary data.
Stack overview The Pixel Web App provides researchers an intuitive way to annotate, store, explore and mine their secondary data analyses, in multi-omics biological projects. It is built upon mainstream open source technologies (see Fig. 2 ). Source code is hosted on the collaborative development platform GitHub (https://github. com/) and continuous integration is provided by CircleCI (https://circleci. com/). More precisely, the Pixel Web App uses the Python Django framework. This framework is based on a modeltemplate-view architecture pattern, and data are stored in a PostgreSQL (https: //www. postgresql. org/) database. We have built a docker image for the Pixel Web App. Other containers, Nginx (to serve the Django application) and PostgreSQL rely on official docker images. Each installation/deployment will result in the creation/execution of three docker instances: one for the Pixel Web App, one for the PostgreSQL database and one for the Nginx web server.
Minimal configuration and dependencies The Pixel Web App can be deployed on Linux and MacOS operating systems (OS). Deployment on Windows is possible, but this situation will not be described here. Minimal requirements are: (i) 64 bits Unix-based OS (Linux/MacOS), (ii) Docker community edition >v18, (iii) Internet access (required in order to download the Docker images) and (iv) [optional] a web server (Apache or Nginx) configured as a reverse proxy.
Installation A step-by-step tutorial to deploy the Pixel Web App can be found in the project repository (https://github. com/Candihub/pixel/blob/master/docs-install/how-to-install. md) together with a deploy script. To summarize, this script runs the following steps: • Pull a tagged image of Pixel (web, see docker-composer file), • Start all instances (web, db and proxy) recreating the proxy and web instances. Collect all static files from the Django app. These files will be served by the proxy instance. • Migrate the database schema if needed (to preserve existing data). Note that further technical considerations and full documentation can be found on GitHub repository associated to the Pixel project (https://github. com/Candihub/pixel/ tree/master/docs).
Definition of terms: Omics Unit, Pixel and Pixel Set In the Pixel Web App, the term ''Omics Unit'' refers to any cellular component, from any organism, which is of interest for the user. The type of Omics Unit depends on the HT experimental technology (transcriptomic, proteomic, metabolomic, etc. ) from which primary and secondary datasets were collected and derived (Fig. 1A ). In this context, classical Omics Units can be transcripts or proteins, but any other cellular component can be defined as, for instance, genomic regions with ''peaks'' in case of ChIPseq data analyses  (Merhej et al. , 2014). A ''Pixel'' refers to a quantitative measurement of a cellular activity associated to a single Omics Unit, together with a quality score (see Fig. 1A ). Quantitative measurement and quality score are results of statistical analyses performed on secondary datasets, e.
Functionalities to annotate, store, explore and mine Pixel Sets Pixel Sets are obtained from secondary data analyses (see Fig. 1A ). Their manipulation with the Pixel Web App consists in (i) their annotation, (ii) their storage in a database, (iii) their exploration and (iv) their mining (see Fig. 1C ). This represents a cycle of multiple data analyses, which is essential in any multi-omics biological project. These different steps are detailed in the following.