dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was financed by  PTDC/CTM-BIO/4043/2014  and by  Portuguese  funds  FCT UID/BIM/04293/2019 ,  EXPL/CTM-BIO/0762/2013  and by  L'Oreal Portugal/FCT/UNESCO. Authors wish to thank  FCT  for  Inês C. Gonçalves  research position ( IF/01479/2015 ),  Catarina L. Seabra PhD  grant ( SFRH/BD/89001/2012 ) and  Patrícia C. Henriques PhD  grant ( SFRH/BD/120154/2016 ). The authors acknowledge the support of the  Bioimaging Center for Biomaterials and Regenerative Therapies  (b. IMAGE), member of the  PPBI  ( PPBI-POCI-01-0145-FEDER-022122 ), for the imaging data acquisition using equipment maintained by the center, the support of the BioSciences Screening i3S Scientific Platform, CCGEN and  Paula Magalhães  for the help during the PCR analysis.
Milbury et al. (2015)  simulated the formation of complex craters using the iSALE shock physics code  (Collins et al. , 2004; Wünnemann et al. , 2006)  and investigated the resulting crater porosity by analyzing the computed Bouguer gravity. While preimpact target porosities up to a maximum of 7% result in negative Bouguer anomalies (indicating higher postimpact porosities), initial porosities greater than 7% result in positive Bouguer anomalies (indicating lower postimpact porosities). In target materials with high preimpact porosity, the impact induced shock wave crushes out preexisting fractures and compresses the material  (Wünnemann et al. , 2006) , causing a reduction of porosity  (Milbury et al. , 2015). At further distances, the preexisting porosity remains intact  (Milbury et al. , 2015) , caused by a more efficient decay of the pressure wave in porous targets  (Love et al.
Areas covered with mare were not considered in our study given that the density structure of the crust in these regions is complex, with dense basalts overlying less dense highland materials  (Gong et al. , 2016). In order to prevent dense mare basalts from biasing the results, we rejected estimated bulk densities where more than 2. 5% of mare were present in the analysis region. All spherical harmonic analyses were performed using the freely available python-based pyshtools software package  (Wieczorek & Meschede, 2018) , and we make use of perceptually uniform colormaps generated by  Crameri (2018). After obtaining the bulk density, the porosity can be estimated if the grain density is known independently. Huang and Wieczorek (2012)  showed that the grain density of common lunar rocks has a linear dependence on both FeO and TiO 2 abundances.
The Shapiro-Wilks test was used for normality analysis of the parameters. As most of the continuous variables were normally distributed, means and SDs with their 95% confidence interval (CI) were reported. Non-normally distributed variables were analyzed using a Mann-Whitney test. Frequencies were reported as percentages. T tests were used to compare independent groups. No paired data were compared. Kaplan-Meier curves for time elapsed between the onset of pVT and first defibrillation attempt were estimated and compared using the log-rank (Mantel-Cox) test for bivariate survival analysis. For the secondary analysis, we evaluated the time elapsed between the onset of pVT to subsequent defibrillation attempts and the delivery of both drugs. For normally distributed variables, means and SDs with 95% CI were reported.
Non-normally distributed variables were analyzed using a Mann-Whitney test. Frequencies were reported as percentages. T tests were used to compare independent groups. No paired data were compared. Kaplan-Meier curves for time elapsed between the onset of pVT and subsequent defibrillation attempts and delivery of both drugs were also estimated and compared using the log-rank (Mantel-Cox) test for bivariate survival analysis. Errors in cycles of chest compression-ventilation were measured as the deviation in percent from the experimental time spent in seconds compared to the 2-min duration recommended by AHA. Wrong defibrillation or drug doses were measured as a deviation from the amount of energy delivered in Joules or drug doses in milliliters compared to AHA recommendations. A χ2 test was used to assess the relationship between absolute errors in defibrillation and drug doses expressed as categorical variables.
Wrong defibrillation mode was also measured. Absolute deviations were also analyzed. The mean (SD) difference in deviation obtained with each method was reported with a 95% CI. A t test for unpaired data was used to compare interventions. Mean differences were reported by randomized group. Univariate linear regressions analysis with 95% CI were performed to assess whether time to initiation of chest compression, defibrillation attempts and drug delivery were associated with the number of postgraduate years or prior resuscitation experience as a provider in real-life and simulated environments. Means and SDs were determined for the perceived stress and satisfaction scores of individuals derived from the Likert-scale questionnaire and reported with descriptive statistics. A P value less than.
Methods: The study was conducted in 2 phases. The initial phase was a quantitative analysis of routinely collected primary care data from the Oxford Royal College of General Practitioners Research and Surveillance Center (RCGP RSC) sentinel network database. AF cases from 2009 to 2019 were identified. The study investigated the impact of the use of anticoagulants on complications of AF over this time period. We used this dataset to examine how AF was managed in primary care during the last decade. The second phase involved development of an online dashboard for monitoring management of AF in general practice. We conducted a usability evaluation for the dashboard to identify usability issues and performed enhancements to improve usability.
Data Source The initial phase involved a repeated cross-sectional retrospective analysis of data extracted from the RCGP RSC database, a surveillance network, which collects data from computerized medical records (CMR) from more than 3. 5 million patients and over 300 general practices in England. CMRs are used during consultations to record patient conditions and prescriptions  [12, 13]. Anonymized clinical coded data from participating general practices were uploaded weekly to a secure network at the Clinical Informatics Research Group  [12, 14]. This study used NICE guidelines as the standard to compare the management of AF across this database.
Case Ascertainment We used Read codes and CTV3 codes used in primary care CMR systems to identify patients with AF. Pay-for-performance (P4P) for chronic disease management has improved data quality related to AF in the recent past  [15]. We excluded patients who had a previous stroke and those not on British National Formulary-recommended dose of DOAC. We included patients over 18 years with code for the diagnosis of AF who had been registered at any point between 2008 and 2019.
Statistical Analysis Analysis of data extracted from general practice CMR was carried out using the statistical software R. We studied the influence of the anticoagulation regimen by evaluating the cause-specific hazard ratio and the subdistribution hazard ratios of both events. Additionally, we estimated cumulative incidence for both events by direct regression, utilizing the inverse of the probability of censoring weights method  [15]  with time-varying effects  [20]. We will report and interpret both cause-specific and subdistribution analyses. The cause-specific hazard ratio is often interpreted as estimating etiological association, estimating associations between covariates, and the rate at which events occur in those participants who are event-free. The cause-specific hazard ratio can be interpreted as a rate ratio.
Phase 2: Development of a Dashboard for Monitoring AF We developed an online dashboard for the general practices to understand the quality of their data across a variety of AF-related indicators. The AF dashboard provided 4 categories of information: (1) case ascertainment-incidence, prevalence, standardized prevalence, and any indicators related to P4P prevalence; (2) indications for therapy and risk factors; (3) management choices; and (4) quality. The dashboard was developed using Tableau data visualization software (Salesforce Inc). The initial dashboard is hosted on the public dashboard cloud server and accesses a publicly accessible database server (located within information technology infrastructure of the University of Oxford), which hosts only aggregated data to comply with the requirements of information governance.
Data Capture and Analysis We recorded participant feedback and screen activity using GoToMeeting screen sharing software, version 10. 5 (LogMeIn Inc). The audio component of the recordings was exported and transcribed by a professional transcription service. The transcripts were analyzed using NVivo, version 12 (QSR International), a qualitative analysis software. We followed a 3-step approach to analyze the transcripts. 1. Mapping verbalized tasks to sections: Each verbalized task description was extracted and mapped to the corresponding section of the dashboard. A "verbalized task" is the narrative description provided by a participant when interacting with particular component in the dashboard. We organized the verbalized tasks according to the template given in (Table  S1  in Multimedia Appendix 1).
Ethical Considerations For phase 1, approval was granted by the research committee of the RCGP RSC. The study did not meet the requirements for a formal ethics board review as per the NHS Health Research Authority's research decision tool  [24]. The study was conducted in line with the REporting of studies Conducted using Observational Routinely-collected Data (RECORD) guidelines  [25]. For phase 2, personal data were not collected from the study participants. Participants were informed that their verbal responses and screen activities would be recorded. All participants received oral and written information about the study.
Discussion The RCGP RSC sentinel network has a large dataset, which is representative of information regarding primary care practices on management of AF. The quantitative analysis will report on the current trends of anticoagulant prescribing using a real-world data set in the United Kingdom and the overall population effect of the use of DOAC use. Missing/incomplete data could be a potential issue in this analysis. We intend to reduce the effect of this issue by incorporating an ontological approach, considering practice level variations in the anticoagulation prescribing. The qualitative analysis of the dashboard will indicate the priority of sections that require an improved user experience. We intend to enhance the dashboard using the outcomes of the usability study and deploy to general practices in the RCGP RSC sentinel network.
For example, at closest approach, EUI and PHI will provide very-high-resolution images (0. 28 equivalent from Earth) and are capable of observing at high cadence (down to sub-second). To reduce their downlink volume, these instruments are capable of data selection and extensive onboard processing, by including internal memory equivalent to or larger than their TM orbit allocation. On the other hand, SPICE will perform little onboard data selection or processing, but flexibility of operation comes from the large parameter space offered by it being a spectrograph. Conversely, the Spectrometer/Telescope for Imaging X-rays (STIX) and the Solar orbiter Heliospheric Imager (SoloHI) have been designed to mostly provide observations of synoptic type.
The SOOP concept will be especially relevant for the RS payload as most instruments have many different observation modes which typically vary significantly in data output depending on the science goal being addressed. Mission planning is driven by the six-month planning periods after each of the European Space TRACKing (ESTRACK) network User Group meetings. There are several levels of Solar Orbiter planning cycles  (Sanchez et al. , in prep. ) , as summarised here: 1. Mission Level Planning (MLP): the Solar Orbiter Science Working Team (SWT) defines top-level science activities for the entire mission (Science Activity Plan, SAP), as well as detailed science goals for each orbit. 2. Long Term Planning (LTP): given input from the SWT, the SOWG defines a coherent mission-level observing plan for a given orbit after ground station allocation has been fixed; it is assisted by the Science Operations Centre (SOC) at the European Space Astronomy Centre (ESAC), Madrid, which provides detailed information on the resources available.
2008 ) suite of instruments on the Solar-TErrestrial RElations Observatory (STEREO,  Kaiser et al. 2008 ) mission. Some instruments can overwrite data which did not emanate from trigger events. All instruments can receive and communicate to such identifications onboard. The means of communication between instruments is described in Sect. 3. Three instruments have triggers in their onboard systems. The EUI has a flare trigger responding to intensity enhancements in EUI/FSI; STIX has a flare trigger responding to flares in the X-ray; and Metis has a CME trigger that responds to enhancements in white light emission off-limb. All instruments can share their coordinates and timings of the events and respond appropriately. One example of a response could be to prioritise these data for download.
Inter-instrument communication (Service 20) An inter-instrument communication (IIC) service has been established on Solar Orbiter to allow instruments to cross-share a limited set of parameters, summarised in Table  2. The IIC is implemented through the use of a dedicated TM packet that each instrument sends to the computer (OBC) with a fixed cadence. All received parameter data are gathered in one data pool. These are extended with extra parameters based on S/C events (like pointing information) and are then communicated back from the OBC to the instruments through the so-called Service 20 TC packets. This service will mainly be used to pass on event detection flags which may sequentially trigger actions in the receiving instruments to safeguard or prioritise data which are likely to include a solar event.
The detection algorithm of the Extreme Ultraviolet Imager and actions based on inter-instrument communication content The EUI will run detection algorithms on the images generated by FSI in both wavelengths. It will broadcast, both for EUI/FSI 304 and EUI/FSI 174 , whether an event has been detected, the time and location (with respect to the instrument boresight) of the detection, and a rough estimate of the brightness of the event. The detection algorithm is based on the SoFast activity detector 1   (Bonte et al. 2013). It uses thresholds on macropixels and a set of rules to minimise the number of false detections. It must be noted however that due to the limited space in the memory of EUI and its automated onboard data management, an EUI detection does not necessarily mean the EUI images make it to the ground.
If later a more significant event is detected, it may potentially overwrite the earlier ones. Also, the detection parameters will need to be adjusted in-flight to take into account the properties of the real data, and any effects, for example those caused by the varying size of the Sun. In turn, EUI will listen to flags broadcast by STIX and Metis. It will combine the information from these two instruments with its own event detection using simple logic (e. g. STIX sees a flare and EUI sees a flare in the same region) to prioritise its data. Also, EUI is planning on using the PHI jitter information for data prioritisation.
Low-latency data As explained in  Sanchez et al. (in prep. ) , scientific data generated onboard are assigned different downlink priorities. The need to define a set of high-priority data became clear when simulations of the fill state of the onboard Solid State Mass Memory (SSMM) revealed that the maximum capacity of the SSMM will likely be reached several times during the mission, resulting in high data latencies. In order to minimise the effect of data latency on the instrument operations planning and performance checks, a minimal set of so-called low-latency (LL) data has been defined for each instrument. The LL daily volume has been necessarily restricted to 1 MB per instrument (2 MB in exceptional cases), to guarantee that it can be downlinked at each S/C contact, even at times of poor communications links (at the far side of the Sun, close to 2 AU from Earth) and taking into account that S/C and instrument housekeeping data (HK) will be brought down first.
The LL dataset fulfils three use cases: 1. It allows basic checks of instrument performance and science data quality (i. e. avoids the up to six-month delay in assessing instrument performance). 2. It allows data selection for those instruments with onboard data buffers or can help (re-)prioritise data in instrument internal memories. 3. It allows the pointing profile to be readjusted and/or retargeted when tracking solar features. A special use case of remote-sensing LL data is the precursor dataset that will be acquired before the start of some RSWs. As described in  Sanchez et al. (in prep. ) , several mission science objectives that require RS observations make it necessary to perform precursor observations before the start RS windows to enable the SOC to choose an optimal fine-pointing profile for the S/C that is commensurate with the pre-planned science activity (related to LL use case 3 defined above).
As this requires a fast turn-around time between data acquisition and S/C pointing updates, the data will be downlinked as part of the daily TM dump. Table  5  lists the sets of low-latency data that have been defined for the RS payload. The numbers between square brackets refer to the use cases defined above. The table shows that in general the low-latency concepts adopted by RS instruments are more complex and less continuous than the approach of the in-situ instruments (see  Walsh et al. 2020). This is partly due to the noncontinuous way of operating, and the variety of science goals assigned to the RS windows. For most instruments, the LL data volume and/or content is likely to vary over the mission and contains data which are specifically designed for the LL data stream. Nevertheless, at all times, the LL data need to fit in the small TM allocation to guarantee prompt retrieval.
Common FITS headers content Solar Orbiter science data are organised into two categories: -remote sensing images and spectra which are stored in the Flexible Image Transfer Standard (FITS;  Pence et al. 2010)  format widely used within the astronomical community; -and in-situ and radio data which are stored in the Common Data Format  (CDF; Goucher et al. 1994 ) used by the particles and fields communities. Most Solar Orbiter science goals are based on coordinated analysis between multiple instruments, and depend on interoperability between these two quite distinct scientific communities. It is therefore crucial for the scientific outcome of the mission, and for the whole scientific community, to make the full range of Solar Orbiter data easily accessible and interpretable by defining data products that are consistently formatted in a transparent and standard manner.
2017). In some cases, the evolving FITS standard necessitated a deviation from common practice. For example, the keywords XPOSURE and TELAPSE were adopted in place of the older EXPTIME keyword to be compatible with the WCS time standard  (Rots et al. 2015). Not all keywords in the Solar Orbiter metadata standard are required to appear in every FITS header, as some are only appropriate for certain kinds of data. However, when these keywords are used, they must conform to the definition in the metadata standard. A complete list of adopted keywords is given in Table  6. Along with basic FITS files, the metadata standard also includes support for IMAGE and binary table (BINTABLE) extensions. A function of the Solar Orbiter metadata standard is to document the context in which the data were taken.
The information contained in Solar Orbiter FITS headers depends on the data level. In general, complete metadata, including S/C attitude, are only available beginning with Level 2 (L2). Low-latency files (LL01, LL02, etc. ) used for operations planning differ from their normal science quality equivalents in that only preliminary calibrations have been applied. During times of low downlink performance, low-latency data may be the only data available for several months. In this case instrument teams may choose to produce an enhanced version of the lowlatency data with better calibration and more complete metadata in the header, which would then qualify as L2 data or above.
Numerical Homogenization Method: Predicting the properties of composite materials taking into account the macroscopic and microscopic scale requires the preparation of a clean code in most cases. But with the evolution of work in this area certain programs are also able to carry out this operation  [5]. Numerical methods of homogenization are numerous and we can cite the one based on a direct analysis by finite elements or the one based on the mean field. This study is carried out in this work using the Digimat software which offers the opportunity to test these two techniques.
Multiscale modelling assumptions: The calculation of the effective properties of the materials is made according to certain assumptions which are: 1. the microstructural parameters are known 2. the mechanical characteristics of the two phases of the composite are known 3. both phases have ideal grip The elastic properties of the two phases are presented in the following table: Multiscale modelling of the elasticity module of the composite using the Digimat MF model: Digimat MF is a tool for estimating the elastic properties of a composite material. It simulates the behaviour of the elastoplastic behaviour of composite materials using the Huber-Mises-Hencky criterion (HMH). This medium field homogenization technique uses the Mori-Tanaka method. The boundary conditions and the applied deformation are equal to 0.
Multiscale modelling of the elasticity module of the composite using the Digimat FE model: Digimat FE is a digital homogenization tool that uses the finite element method (FEM). It allows to generate a mesh on a Representative Volume Elementary. This mesh will then be requested with well-defined boundary conditions to calculate the homogenized properties. We generate Representative Volume Elementary (RVE) for several volume fractions by the Poisson random process used by Digimat FE. The Figure  3.
Patients Consecutive patients with BmCRPC initiating treatment with abiraterone from March 2014 to March 2015 were prospectively included in one center. Treatment initiation with abiraterone was validated by a multidisciplinary staff. This study was approved by our institutional ethics committee (29BRC17. 0189). All participants provided written informed consent. Initial clinical data including age, PSA level, Gleason score, and previous treatment were recorded.
The energy window was set at +/-7. 5%, centered on the photon energy peak of Tc-99m (140 keV). SPECT data were reconstructed using an Ordered Subset Conjugate Gradient Minimization (OSCGM) algorithm allowing SUV quantification [xSPECT R  (13) , Siemens], with the following parameters: 8 iterations and 6 subsets with a 256 × 256 matrix (pixel size 1. 9 × 1. 9 × 1. 9 mm), and a 10 mm FWHM Gaussian post-filter. Scatter correction was applied. CT-based attenuation correction was applied to SPECT images. The CT acquisition was performed immediately after the SPECT acquisition, from the upper cervical spine to the proximal femora. The CT parameters were as follows: pitch 1, tube voltage 130 kV, automatic mAs control (reference mAs 90), slice thickness 3 mm, matrix 512 × 512, field of view 50. 2 cm.