dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulating the open flux at the Sun While remote solar observations allow us to infer the open solar magnetic flux in the solar corona, we are not able to measure it directly. In an alternative approach, we employ a thermodynamic MHD model (e. g. ,  Mikić et al. 2018)  for CR2101, and we create a sequence of simulated AIA images for the same view point of the real spacecraft over the course of the rotation. We apply the CH detection methods to the simulated data, and compare with the "true" open flux (known from the model) to further assess the effectiveness of detection methods in accounting for open flux. The thermodynamic MHD model is briefely described in the Appendix. We performed six detections for the selected CH using CATCH, CHIMERA, PSI-SYNCH, SPoCA, THR and PSI-MDIM. The detections were each done using their native input data and projection.
0. 2 database  (Del Zanna et al. 2015)  and the CHIANTI hybrid abundances  (Fludra & Schmelz 1999) , based off  (Schmelz et al. 2012). Synthetic images are created by integrating the 3D volumetric emissivity along the line of sight from a given viewpoint. The EUV map in Figure  8 (b) was constructed by integrating along radial lines of sight;  Linker et al. (2017)  described a detection test with PSI-SYNOPTIC on a similar EUV map. While this comparison yielded useful insights, in general, such a map is more favorable for detection than real images from spacecraft instruments. While obscuration of CHs from bright loops can still occur, this line of sight occurs only at disk center. Away from disk center (especially at higher latitudes) more obscuration may occur, and polar regions are especially foreshortened.
To test CH detection techniques under more realistic conditions, with a dataset more akin to those actually produced by AIA, we created a sequence of synthetic emission images in multiple wavelengths from the MHD simulation as observed from the vantage point of SDO/AIA. The B0 angle (i. e. , the heliographic latitude of the central point of the solar disk) of the Sun is included in the geometry. A set of images was created approximately every six hours, for a total of 111 image sets. As described in section 4. 1, smoothing of the magnetic map reduces the presence of small-scale, mixed polarities, and these provide important contributions to the complexity of real emission images. The amount of smoothing of the map is in turn determined by the available resolution for the MHD simulation, which strongly influences the computational cost.
ACKNOWLEDGMENTS We thank the  International Space Science Institute  ( ISSI, Bern ) for the generous support of the ISSI team "Magnetic open flux and solar wind structuring in interplanetary space" (2019)(2020)(2021). JAL, RMC, and CD were supported by the  NASA   Heliophysics Guest Investigator program  (grant  NNX17AB78G ), the  NASA HSR program  (grant  80NSSC18K0101 & 80NSSC18K1129 ),  AFOSR  (contract #  FA9550-15-C-0001 ), the  NSF   PREEVENTS program  (grant  ICER1854790 )), and the STEREO SECCHI contract to NRL (under subcontract  N00173-19-C-2003  to PSI). Computational resources were provided by  NASA's NAS (Pleiades)  and  NSF 's XSEDE (TACC & SDSC). BV acknowledges the financial support by the  Croatian Science Foundation  under the project  7549  " Millimeter and submillimeter observations of the solar chromosphere with ALMA.
"SJH was supported, in part, by the  NASA   Heliophysics Living With a Star Science Program  under Grant No. 80NSSC20K0183. EA acknowledges support from the  Academy of Finland  ( Postdoctoral Researcher Grant   322455 ). MO is funded by part-funded by  Science and Technology Facilities Council (STFC)  grant numbers  ST/R000921/1  and  ST/V000497/1. CS acknowledges support from the  Research Foundation -Flanders  (FWO, strategic base PhD fellowship No. 1S42817N ), and from the  NASA   Living With a Star Jack Eddy Postdoctoral Fellowship Program , administered by  UCAR's Cooperative Programs for the Advancement of Earth System Science (CPAESS)  under award no. NNX16AK22G. ES acknowledges support from a PhD grant awarded by the  Royal Observatory of Belgium. JP acknowledges support from the  SolMAG  project ( ERC-COG 724391 ) funded by the  European Research Council (ERC)  in the framework of the  Horizon 2020 Research and Innovation Programme , and the  Finnish Centre of Excellence in Research of Sustainable Space  ( Academy of Finland  grant number  312390 ).
2" W). Six to 7 days and 16 to 23 days separated the outdoor walking session from the 6-min walking test in 12 and 6 participants, respectively. The three walking places used in the study had small differences in terms of altitude and declivity (altitude change/travelled distance × 100) profiles. This can be observed using the interactive Supplemental Digital Content (SDC) 1 file that was built using corrected altitude data (rescaled to a minimum of 0) and GPS distance data recorded from three participants, with data interpolated every 20 m (please see the following subsection for detailed explanations regarding computation of corrected altitude and GPS distance). Of note, the main difference between the walking courses was that in Angers, the beginning and the end of the walking course was associated with greater declivity than in the other walking places.
As during the 6-min walking test, the participants could use an assistive device to complete the test (12 participants used a Nordic pole, 3 participants used a cane or a crutch, and 3 used no assistance). During the outdoor walking session, the participants were equipped with a DG100 GPS receiver (GlobalSat, Taipei, Taiwan)  [14, 17]  that is composed of a unit (8. 0 × 5. 5 × 1. 8 cm, weight ~60 g) and an antenna that was placed on the right shoulder of the participants. Six different DG100 units were used, with 2 to 5 outdoor walking sessions completed with each unit. The DG100 was set to record speed (Doppler method) and coordinates (latitude, longitude, altitude) at a 1-Hz sampling rate with the European Geostationary Navigation Overlay Service function enabled. The DG100 units were set and data were downloaded using a personal computer and manufacturer's software (Data Logger Utility, version 1.
1). The Data Logger Utility software was also used to export the GPS data to gpx. files for further analysis. The meteorological information at the time of the walking session was recorded using the Weather Underground website (https://www. wunderground. com, accessed on March-April 2019). Both before the 6-min walking test and the GPS session, the participants completed the Fatigue Severity Scale  [18]  to provide information regarding their state of fatigue during the week where the tests were completed.
GPS Data Analysis We analyzed GPS data for participants who completed the outdoor session while respecting the provided rules. Analyses were performed using R software (version 3. 6. 0 (26 April 2019)  [19] ) and consisted of the followings steps (steps 2, 3 and 4 are based on previous works that studied the validity of a speed processing methodology for detecting walking and stopping bouts and estimating walking speed and distance  [13, 14] ):.
• Step 1, preparation of the dataset to have the required data in an appropriate format for further analysis (this step included the addition, in the dataset, of the altitude data from the French National Institute of Geography map projections obtained using CartoExploreur3 [version 3. 11. 0, build 2. 8. 10. 14, Bayo, Ltd. , Abrest, France] and that corresponded to the latitude and longitude data recorded by the GPS device; an illustration of the procedure to get these data is provided elsewhere  [20] ).
From the processed GPS data file, we calculated, for each participant and where appropriate, the following outcomes: the session duration (from the beginning of the first walking bout to the end of the last walking bout), the total walking time, the total walking distance, the mean speed calculated using all the detected walking bouts, and the number of stops due to symptoms during the session. For the participants who stopped at least once during the session due to symptoms, we determined the greatest walking distance performed over a detected walking bout during the session (this parameter was considered as the session outdoor maximal walking distance in the present study), the mean and coefficient of variation (CV) of the performed walking distances, the mean and the CV of mean walking speed adopted during the walking bouts, the mean and the CV of stop durations, and the walking bout during which the greatest walking distance was recorded.
Statistical Analysis Statistical analysis was conducted using R software (version 3. 6. 0 (26 April 2019)  [19] ). Numeric variables are shown as median and interquartile range [IQR], and categorical variables are shown as counts and proportions throughout the article. We calculated 90% confidence intervals (90% CI) for the medians and proportions related to the variables investigated during the outdoor walking session only. For the medians, the 90% CIs were calculated using the BCa method (999 replicates) implemented in the "groupwiseMedian" function of the "rcompanion" package  [21]. For the proportions, the 90% CIs were calculated using the Clopper and Pearson method implemented in the "binom. test" function of the "stats" package  [19]. We explored the construct validity of outdoor maximal walking distance (using the data from the participants who had to stop walking during the session) and outdoor usual walking speed (using the data from all the participants with valid results) as functional outcomes by investigating Pearson and Spearman correlations (and their respective 90% CIs) between those variables and the 6-min total walking distance.
Data and R Code Availability All the participants' GPS and corrected altitude data files, the R code that has been implemented to process these files, and the R code created for statistical analysis, are available in an Open Science Framework (OSF) repository (https://osf. io/tp37b, lastly updated on 17 April 2021). Of note, a personal Application Programming Interface (API) key is needed to get the map related to the GPS data when using the R code, but the proposed R functions allow getting the data visualizations without the map. We also developed a shiny app available on the Web at https://pydemull. shinyapps. io/gps-walk to allow people who do not work with R to easily perform their GPS walking data analysis thanks to the same procedure as that used in the present article. In case the address at which the app is stored changes in the future, we provided the full code of the app in a Github repository (https://github.
The Outdoor Walking Capacities The outdoor walking sessions were completed with the following meteorological conditions: temperature of 13  [11] [12] [13] [14]  • C; relative humidity of 58 [55-66]%; wind speed of 19  [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21]  km/h. Among the 12 participants included in final analysis, 3 (25%) used no assistive device, 2 (17%) used a cane or a crutch, and 7 (58%) used Nordic poles. The FSS scores obtained at the group level at the time of the GPS session were similar to those obtained at the time of the 6-min walking test; however, some patients showed either a marked increase or a marked decrease in their FSS score between the 6-min walking test and the GPS evaluation periods (see  SDC 2). Most of the GPS data (>99%) were acquired with 1-s epochs for each participant, as expected.
Only one participant (#9, see https://osf. io/rcjds for figure, lastly updated on 17 April 2021) had aberrant coordinates and speed data that corresponded to a stopping bout we detected using the speed processing methodology. It was deemed these spurious data were not a problem for getting reliable results since it seemed no displacement occurred during this period, as inferred from the coordinates that were the same at the beginning and at the end of this period. Despite the initial target of completing a session of 40-60 min, eight participants stopped the session before reaching this target due to fatigue or personal time constraints. Thus, the median of the session time was only 30. 4 min. The results regarding the variables measured during the outdoor walking session are shown in Table  2.
This last information could be worthwhile to avoid the implementation of an unnecessarily time and effort-consuming walking session. Regarding the methodological issues we encountered, the absence of a resting phase in the GPS data at the beginning of the session for two participants was of importance since it could threaten the validity of the maximal walking distance recorded for those participants. This problem highlighted the need of graphical analysis when dealing with GPS data recorded in the present context, even if the R code we provided could work and provided results despite this loss of data. It could be good practice to determine a precise starting point of the walking session that could be recognized on a map, and to compare it, when necessary, with the recorded starting point to have confidence in the true beginning of the session observed in the GPS data.
This could also be the case for outdoor walking speed in comparison with outdoor maximal walking distance, but this should be investigated in reliability studies. The present work presents several strengths. First, our results were obtained under normal working conditions related to three different healthcare or sports structures and highlight the possibility, and also the challenges, of conducting GPS-measurements in real-world contexts. Second, all of our GPS results are provided in a context that is transparently presented for all the participants, with the possibility to make judgements about the characteristics of the walking courses (i. e. , based on corrected altitude data, longitude/latitude data, and the corresponding map) and about the quality of the GPS signals. Third, the present paper proposes fully open materials with de-identified information, allowing the reproduction of GPS data analysis and statistical analysis.
Of note, if used for new investigations, the R code could need some adaptations depending on the structure of the gpx. files, and on the use or not of corrected altitude data. Several limits have also to be acknowledged. First, during the outdoor walking session, some participants choose to use a walking aid although they were able to walk without it. Moreover, some participants could perform their walk relatively close to other participants. We do not know to what extent our GPS results may have been influenced by these elements, but this should be taken into account for potential comparisons with future studies. Second, our outdoor walking sessions had an upper time limit, forcing the participants to stop walking even if they could have walked a longer distance. Thus, our ability to detect and quantify outdoor maximal waking distance was restricted to the most disabled participants.
Acknowledgments: We thank the participants and the structures for their help to conduct this study: the rehabilitation clinic " Les Capucins " (Angers), the sports association " Bonne Garde" (Nantes) , and the sports association " L'alerte de Méan" (Saint-Nazaire ). In the present study, the members of the  SOCOS group are Pierre-Yves de Müllenheim ,  Florian Congnard , and  Bénédicte Noury-Desvaux.
RAD-library preparation and sequencing were performed at the MGX-Montpellier GenomiX platform (Montpellier, France). DNA was digested with the Pst1 enzyme and the library was prepared according to Baird and Etter' protocol  47  in a slightly modified version. Paired-end RAD-sequencing was performed on a 2 lanes flow cell of an Illumina HiSeq2500 in a rapid mode so that reads (125 bp) were expected to be of high quality with no missing base (N content). We obtained 299 million sequences, comprising R1 and R2 reads for each sequenced fragment. Adapters were removed from the reads. Read quality control, alignment and dataset generation. Read quality was assessed with FastQC v0. 11. 9 (http://www. bioinformatics. babraham. ac. uk/projects/ fastqc/). The per base sequence quality was high across all reads (no lower than 36 for R1 and 32 for R2) with an average quality score of 39 (40 being the maximum).
5%). Datasets were stored in a VCF file (containing all the SNPs found in the alignment) and a fasta file (containing the two alleles found at every locus for each sample). To run DILS-ABC inferences, Stacks fasta files were converted to another fasta format compatible with DILS (https://github. com/CoBiG2/RAD_Tools). Demographic inferences. Eight categories of demographic models were compared, according to temporal patterns of introgression. This was done to answer two questions on gene flow in Morpho: (1) is there ongoing migration between M. helenor and M. achilles? (2) do M. helenor and/or M. achilles exchange alleles with M. deidamia? This was assessed by an ABC approach using a version of DILS adapted to samples of three populations/species  32. Since Stacks does not report monomorphic RAD loci, the ABC analysis was conditioned in the same way, by excluding monomorphic loci from the simulations.
For each model, 50,000 simulations using random combinations of parameters were performed. Parameters were drawn from uniform prior distributions. Population sizes were sampled from the uniform prior [0-1,000,000] (in diploid individuals); the older time of split was sampled from the uniform prior [0-8,000,000] (generations); ages of the subsequent demographic events were sampled in a uniform prior between 0 and the sampled time of split. Migration rates 4. N. m were sampled from the uniform prior [0-50]. 4] [55]. On each simulated dataset, we calculated a vector of means and standard deviations for different summary statistics: intraspecific statistics (π for M. helenor, π for M. achilles, π for M. deidamia, θ W for M. helenor, θ W for M. achilles, θ W for M. deidamia, Tajima's D for M. helenor, Tajima's D for M.
We first conditioned the mutations occurring during coalescent simulations by using theta (=4 • N • µ • L i ; where N is the effective population size, µ the mutation rate per nucleotide and per generation; L i the length of locus i). The number of simulated segregating sites for a given locus strongly depends on the coalescent history (i. e the total length of the simulated coalescent tree), occasionally generating monomorphic loci. To confirm that the inferences are not impacted by differences in the number of monomorphic loci in the simulated datasets, we then used an alternative simulation approach, by randomly placing in simulated coalescent trees a fixed number of mutations corresponding to the observed number of SNPs for each locus. Thus, a randomly simulated dataset consists of 2740 loci whose lengths (ranging from 339 to 894 nucleotides) and number of SNPs (ranging from 1 to 91) individually match the properties of the observed loci in the actual dataset.
Since the results drawn from both approaches were similar, we report only the estimations provided by the simulations based on the actual number of SNPs. Comparisons between the two approaches can be found in supplementary (Supplementary Tables  8, 9 ). Statistical comparisons between simulated and observed statistics were performed using the R package abcrf version 1. 8. 1  58, 59. Mark-recapture experiment. To estimate the timing of patrolling activity among Morpho species, we performed capture-mark-recapture between 9 a. m. and 2 p. m. (flight activity in Morpho is drastically reduced in the afternoons at this site) during 17 sunny days. Although on a few days, capture was cancelled because of bad weather annihilating butterfly activity, the 17 capture sessions were mostly consecutives, as they were performed in a 22 days period (Supplementary Table  1  and Supplementary Fig.
helenor and M. achilles in the below-described experiment because butterflies from these two species are morphologically too similar to be identified while flying (Supplementary Fig. 13 ). After the 17 nearly-consecutive days of capture, one day of capture was repeated every 2 weeks during 2 months in parallel to the dummy experiment (described below), to verify that temporal activity was stable over time (Supplementary Fig. 13 ). Estimating population size from mark-recapture data. Based on capturerecapture histories, we estimated individual abundance for each species using a loglinear model implemented in the R package Rcapture version 1. 4. 3  60  (Supplementary Fig. 15 ). Given the short duration the sampling period (22 days) relative to the longevity of adult Morpho butterflies (several months  61  ), we used a closedpopulation model assuming no effect of births, deaths, immigration and emigration.
helenor and M. achilles. However, the sister species M. helenor and M. achilles cannot be discriminated during flight, and we thus rely on an indirect method, based on flight hours, to infer the species identity of wild visitors looking as a M. helenor/M. achilles (Supplementary Fig. 13 ). Note that removing data with the highest levels of uncertainty in species identity (i. e. when discarding visits performed in the period where M. helenor/M. achilles temporally overlap) does not quantitively affect our results (Supplementary Fig. 14  and Supplementary Tables  5, 6 ). Using the recorded video, we also measured the duration of the interactions (i. e. the time spent in the cubic space) occurring between patrolling male and the dummy. The ten dummies were each tested during 4 sunny days from 9 a.
Acknowledgements We would like to thank the Peruvian authorities, and in particular SERFOR (the  Servicio Nacional Forestal y de Fauna Silvestre)  for providing the necessary research permits (permit: 373-2017-SERFOR-DGGSPFFS). We thank  Ronald Mori Pezo  and  Gautier Guyot  for their help with the mark-recapture experiment. We are grateful to  Nicolas Navarro  for his advice on the statistical analyses. C. L. R. acknowledges financial support by  Université de Paris  and the  Ecole Doctorale FIRE -Program Bettencourt. This work was also supported by a grant from  Agence National de la Recherche  under the LabEx  ANR-10-LABX-0003-BCDiv , in the programme " Investissements d'avenir "  ANR-11-IDEX-0004-02  to C. L. R and from the  Emergence programme of Paris city council  to V. L. The RAD-sequencing performed at the MGX platform also benefited from the financial support from the  France Génomique National Infrastructure , funded as part of " Investissement d'Avenir " programme managed by the  Agence Nationale de la Recherche  (contract  ANR-10-INBS-09 ).
Code availability Genomic analyses were performed using the statistical analysis platform DILS (https:// eep. univ-lille. fr/en/productions-2/dils-software/). A modified version of DILS adapted for studying divergence among three species/populations was used for this study. Source codes can be freely used from GitHub: https://github. com/Camille-Le-Roy/ DILS_3pop_SNP [https://doi. org/10. 5281/zenodo. 5707446].
Other solutions propose to build cell population by estimating cell parameters from measurable data or from theoretical values. Existing free and open source software such as Chaste  [28]  or CompuCell3D  [29]  are able to propose cell population models based on these parameters to initiate cell clustering as well as growth, division, death, intracellular adhesion, volume and surface area constraints and include dedicated cellular automata for categorizing cells. Nevertheless, those toolkits do not offer the possibility to obtain deformable cell shapes with dynamic radii that would fit accurately experimental outcomes. The CPOP platform proposes dedicated meshes to distort each cell using a dynamic radius to obtain a high-level conformation of cell membranes with their neighbours. Furthermore, CPOP is able to deal with large cell populations (multicellular spheroids) reaching over one million of cells.