dataesr/scientific-paragraphs-categorization huggingface dataset:
Population and Questionnaire The data were collected between 9 April and 24 May 2018 using an online questionnaire sent to members of an online panel (n = 450,000) operated by a generalist market research company (Creatests). The quota method  [24]  was used to obtain a representative sample of French adults aged 18 to 65 years from the 2017 French population, as estimated by the French National Institute of Statistics and Economic Studies (Table  S1 ). The representativeness of the sample was ensured for sex, age, socio-professional category and geographic area of living. A total of 2692 people (1408 women and 1284 men) completed the questionnaire, which took 15 min on average. Participants gave informed consent on the first page of the web survey, and as a reward for completing the questionnaire, they earned points redeemable for vouchers valid in various stores.
Food Intake Data First, participants completed a food frequency questionnaire (FFQ) on their intakes of protein foods. They were asked about their frequency of consumption of protein food groups on a nine-item scale ranging from "never" to ">3 times/day". The 29 groups of protein foods considered in the FFQ covered 84% of the total protein intake of the French population in 2006-2007, as detailed in a previous study  [22]. In addition, they were asked about the usual size of their portions (on a seven-item scale extracted from the SU. VI. MAX photo book  [26] ) of protein food groups that they reported they consumed. The 29 food groups of the present study were sorted from the ones whose modifications of portion sizes increased nutrient adequacy the most, to the ones whose modifications increased nutrient adequacy the least in a previous modelling study  [22].
Identification of Misreports We identified misreports using a three-step method. First, consistency was checked using four questions on portion sizes (about bread, pasta, beef and poultry) that were asked twice in different parts of the questionnaire. A participant was excluded if he/she reported different portion sizes of the same food more than once (n = 195). Second, the plausibility of self-reported frequencies was tested. If the sum of the self-reported intake frequencies was <once/day or >20 times/day (respectively, 2. 5th and 97. 5th percentiles of intake frequency of protein foods in the INCA2 population, as estimated from the dataset), the participant was excluded (n = 61). Finally, the plausibility of intakes was tested using the mean intakes reported in the 2014-2015 INCA3 study  [28].
Secondly, we used the willingness scores as continuous variables, with mixed models using the proc MIXED (SAS Institute Inc. , Cary, NC, USA). Data are means ± SEM. An overall α level of 5% was used for statistical tests. Sample weightings were applied to ensure the representativeness of the sample with respect to sex, age, socio-professional category and geographic area of living, using the Icarus R package. All means and models were thus weighted in this way. When not stated otherwise, all analyses were performed using SAS 9. 4 (SAS Institute Inc. , Cary, NC, USA).
By contrast, participants in the oldest age group were more willing to introduce foods they did not already consume than their younger counterparts, which is inconsistent with data in the literature which show that the elderly are usually more neophobic than other age groups  [47, 48]. This could be explained by the fact that the elderly were aware of the foods being recommended (including vegetables, legumes, grains or fish) but were not used to eating them, unlike younger participants who might not be aware of these foods. This would be consistent with studies showing that elderly people are more likely to follows a Mediterranean diet than younger individuals who are more likely to follow a western diet  [49]. Some limitations and uncertainties of this study need to be underlined. The FFQ used to assess food intakes was not validated by another assessment such as 24h recalls, and the estimates of food intake and contribution to protein intake were partial, because the subjects were only asked to declare their intake of protein food groups.
However, the aim of the estimation of food intake was only to identify dietary patterns based on protein foods consumption only and not to assess the overall diet or nutrient adequacy. The protein dietary patterns identified were thus based on partial information. Indeed, some food groups, such as meat substitutes based on plant protein, were not reported. Furthermore, the questions concerning the willingness to modify the dietary behavior were self-reported and might have suffered from desirability bias, inasmuch as we mentioned to the volunteers that the modifications were likely to lead towards a more balanced diet. BMI values also came from self-reported data only, so they may have been biased  [50]. Summarizing the rating scale scores as a binary variable ("willing" or "not willing") resulted in some loss of information, and this may have produced some simplified results that should be interpreted with caution.
However, because the scores were not normally distributed, this analysis was the most appropriate to tackle the complexity of the data at hand. The portion sizes shown to participants (i. e. , photos of foods presented on plates) did not have the same differences in terms of quantity or perceived energy density. For example, there were 20 g of difference between two portion sizes of nuts and seeds, and 50 g for poultry meat. This could have influenced the willingness to eat a larger portion size, as individuals could have found it easier to eat 20 g more rather than 50 g more. However, this portion size "step" effect was tested in the three models and was not found to be significant. Any liking for foods not consumed by the participants was not recorded, which could have explained the ambivalence in the willingness to introduce new foods (either a strong refusal or a strong approval).
FRAP analysis Wild-type and yip4a yip4b plants expressing ROP2::EYFP-ROP2  (Xu and Scheres, 2005)  were grown vertically on ½ MS agar plates. Five-day-old seedlings were mounted on a slide and overlaid with a 2 mm layer of MS agar to avoid dehydration. The seedlings were imaged with a Zeiss LSM780 confocal microscope using a water 40× objective (C-Apochromat 40×/1. 2 W Corr M27). The rectangular region of interests (ROI) function of the Zeiss LSM software was used to select the area for bleaching. A 10-or 11-cell long strip in the transition zone along the entire width of the root was bleached (10-15 iterations of full power of the 458, 488 and 514 nm laser lines). Bleaching was repeated at different z positions to also ensure complete bleaching of underlying cells. Pre-bleach and post-bleach images were acquired as a z-stack to ensure imaging identical planes.
The software can also run in batch mode, which integrates the SfM workflow with a user-friendly interface, but also because it can be used in batch mode through a Python application programming interface (API) to automate future processing. Numerous studies have successfully utilized Agisoft Photoscan in this manner  [22] [23] [24] [25] [26] [28] [29] [30]. We used the images that were acquired at 08:00 local time. Agisoft was configured to produce a DEM at 10 cm horizontal resolution. The coordinates of the cameras were surveyed with a dGPS and provided as input data to Agisoft. For each date, this process also allowed the generation of an ortho-rectified image of the scene from the photographs of the three cameras using the maximum of intensity at each pixel among the three images. This ortho-image was used in the co-registration step below.
On each date, the random points were measured on the snow cover. The criteria for choosing the points locations was a space of around 5 m for a flat area; the points were densified to approximately 1 m, when a change of slope was noticed. In other words, the locations of the points were randomly selected, but the spacing between he points varied, depending on the topography (closer points where the snow surface was more variable). The number of measured points for each date is shown in Table  2. For the comparison of the total station DEM with the camera DEM, the total station points were converted to a raster, with the same spatial resolution of 10 cm. The points were first converted to triangular irregular network (TIN) and then to a regular elevation grid in ArcMap software (Figure  5 ).
For the comparison of the total station DEM with the camera DEM, the total station points were converted to a raster, with the same spatial resolution of 10 cm. The points were first converted to triangular irregular network (TIN) and then to a regular elevation grid in ArcMap software (Figure  5 ). During the same topographic surveys, the limit of the snow cover was marked using a specific code when acquiring the point coordinates on the edge of the snow patch. These points were assembled as a single polygon to delineate the snow limit.
Conclusions We have studied the accuracy of a new low-cost system to monitor the snow depth in a sinkhole, in the snow-dominated area of Mount-Lebanon. The system is based on three standard compact digital camera photographs, which typically cost USD $100 each. The cost of the system can be further reduced by using a single-board microcontroller instead of the CR200 data-logger, in order to trigger image acquisition. The results indicate that a simple workflow, using Agisoft Photoscan and Ames Stereo Pipeline software, can estimate the snow surface elevation, with an acceptable accuracy between 20 and 60 cm (standard deviation of the residuals), and a low bias (below 50 cm) when there is sufficient stable terrain around the snow patch. We observed that the co-registration worked well with a ratio of stable area to snow area of roughly one third, but this is probably highly-dependent on the distribution of the stable terrain across the scene.
The Agisoft Photoscan software is commercial, and was used in this study because it is efficient and user-friendly, however, open-source alternatives exist to perform the computation of the 3D model with similar accuracy (e. g. , MicMac  [37] ). The system is autonomous and requires only minimal maintenance. It can be fully automated to generate a time series of snow surface DEMs, as the processing is not based on field data, apart from the coordinates of the cameras that need to be surveyed once. In addition, it was possible to setup a near real-time system by transmitting the cameras images using mobile telecommunications networks. This could be useful in supporting the monitoring of the water resource, since the snow volume in the sinkhole can be considered a proxy of the state of the snow resources in the Mzaar area.
Acknowledgments: This work has been supported by the  Research Council at USJ  and the  United Nations Children's Fund (UNICEF). It was carried out in the frame of the ERANET-MED03-62  CHAAMS  project and the  Observatoire Franco-Libanais pour l'Environnement (O-LIFE ). We warmly thank the two anonymous reviewers for their comments and suggestions, which helped improve the paper.
XRD Analysis For phase identification and crystalline nature of biosynthesized AgNPs, powdered BC-AgNPs and AE-AgNPs were subjected to XRD analysis. AgNPs showed peaks at 37. 976°, 44. 1069°, 64. 1987°, and 77. 2926°, while AE-AgNPs showed peaks at 32. 3635°, 36. 7353°, 42. 7714°, 48. 9495°, 50. 7722°, 59. 3511°, 62. 0693°, 69. 1236°, and 72. 0772°. The peaks of BC-AgNPs corresponds to (111), (200), (  220 ) and (311) planes of AgNPs Bragg's reflection and prove that the synthesized AgNPs had a facecentered cubic crystalline structure. Similarly, the peaks of AE-AgNPs at 32. 3635°, 36. 7353°, 48. 9495°, 59. 3511°, 69. 1236°, and 72. 0772° closely corresponds to (122), (111), (231), (241), (  220 ) and (311) planes of AgNPs Bragg's angles. Apart from these peaks, a few unknown peaks were also obtained, which might be due to certain impurities within the synthesized nanoparticles  [25]  also obtained similar sort of XRD data for AgNPs.
HPLC Analysis Samples were loaded into HPLC vials and quantified using Varian high-performance liquid chromatography system equipped with Varian Prostar 230 pump, Meta chem Degasit degasser, Varian Prostar 410 autosampler and Varian Prostar 335 Photodiode Array Detector (PAD) and driven by Galaxie version 1. 9. 3. 2 software (Varian, Le Plessis-Robinson, France). The HPLC grade standards for isolated compounds, i. e. , caffeic acid, chicoric acid, rosmarinic acid, cyanidin, and peonidin, were acquired from Sigma Aldrich. The technique used for separation is adapted from  [24]. For separation, Hypersil PEP 300 C18, 250 × 4. 6 mm, 5 µm particle size prepared by a guard column Alltech, 10 × 4. 1 mm was used at 35 • C. Compounds detection was achieved at wavelengths of 320 nm and 520 nm. The mobile phase was composed of A = HCOOH/H 2 O, pH = 2.
XRD Analysis For phase identification and crystalline nature of biosynthesized AgNPs, powdered BC-AgNPs and AE-AgNPs were subjected to XRD analysis. AgNPs showed peaks at 37. 976   220 ) and (311) planes of AgNPs Bragg's angles. Apart from these peaks, a few unknown peaks were also obtained, which might be due to certain impurities within the synthesized nanoparticles  [25]  also obtained similar sort of XRD data for AgNPs. Size calculated by Scherrer equation of AE-AgNPs 50. 9 nm and BC-AgNPs 43. 6 nm. Figure  2A ,B show the graph from the XRD analysis of the AgNPs.
Inclusion Criteria After institutional review board approval, all patients with bAVMs treated with SRS at the University Hospital of Nancy, France were prospectively included in a database. For the present study, patients imaged between March 2012 and May 2018 were included if they met the following criteria: 1) They had a bAVM treated by embolization and radiosurgery or radiosurgery only, 2) they were imaged during follow-up with SWAN at 1. 5T or 3T and DSA, 3) both examinations were performed within a time interval of Ͻ6 months and without another treatment session in between, and 4) both examinations were performed at least 12 months after SRS. The treatment strategy for each patient was based on multidisciplinary decisions involving neurosurgeons, radiotherapists, and neuroradiologists.
Excitation of the sample was done using a UV-LED (365 nm; 3 W) and a sensitive blue photo-multiplier tube detecting fluorescent light between 435 nm and 560 nm on a CyFlow Ploidy Analyser (Sysmex, Norderstedt, Germany). 1 ). A 0/1 matrix was generated by automatic band scoring using GELCOMPAR II v. 5. 10 (Applied Maths NV, Sint-Martens-Latem, Belgium) and a screening through 112 parameter combinations comprising different combinations of values for peak minimal profiling (0. 1, 0. 5, 1. 0, 1. 5, 2. 0, 2. 5 and 3. 0), peak minimal area (0. 1, 0. 2, 0. 3 and 0. 4) and band matching tolerance (0. 1, 0. 2, 0. 3 and 0. 4) was carried out using the seven replicate samples. In order to choose the most reliable combination, the Euclidean error, the Jaccard distance, the number of correctly paired individuals, and the phylogenetic resolution score were calculated using a Python script developed by  Holland & al.
(2008). The parameter combination with the highest scores was then chosen for band-scoring in all remaining individuals. To visualize the genetic similarity among individuals, a Neighbor-Net diagram was constructed based on Nei-Li distances  (Nei & Li 1979)  using the software SPLITSTREE v. 4. 14. 6 (Huson & Bryant 2017). Additionally, a Bayesian clustering of individuals was done with the software STRUCTURE v. 2. 3  (Pritchard & al. 2000). For estimation of the optimal cluster number k the method of  Evanno & al. (2005)  was used. Allele frequencies were set to correlated, all individuals were assigned to haploid level to account for the dominant marker system and the mixed ploidy levels in the dataset. The burn-in was set to 50 000 generations and chain length to 500 000 generations. The analysis was run 10 times and the results were averaged using CLUMPP v.
2 ). AFLP fingerprinting -The automatic band scoring procedure with the Python scripts of  Holland & al. (2008)  revealed as the best combination of parameter values a peak minimal profiling of 2. 5, a peak minimal area of 0. 3 and a band matching tolerance of 0. 4. With these parameter values, automatic band scoring yielded 425 bands with an Euclidean error rate of 13 %, a Jaccard error rate of 47 %, a resolution score of 46 % and six out of the seven replicates were consistently paired. As expectable from the flow-cytometrically determined differences in genome size between representatives of Leucanthemum ircutianum (tetraploid) and L. corsicum (hexaploid), representatives of the former species produced on average less bands than the latter ones (88. 9 vs 95. 5, respectively). Five individuals (three from population 444 and two from population 445) showed comparably faint AFLP Willdenowia 49 -2019 banding patterns and were omitted from the subsequent analyses.
Following the method of  Evanno & al. (2005) , the optimal number of clusters in the Bayesian clustering of STRUCTURE was inferred to be k = 2, corresponding to the two species involved, Leucanthemum corsicum and L. ircutianum (Fig. 3 ). There was only a single individual found that was not assigned to one of the two clusters with a posterior probability of PP > 0. 9; i. e. an individual from population 438 (438-01) being clustered to L. ircutianum with only PP = 0. 873. The clear bipartition of the dataset was also revealed in the Neighbor-Net network reconstruction based on Nei-Li distances (Fig. 4 ). These two analyses also clearly showed that the two subspecies (and three forms) of L. corsicum do not represent genetically distinct lineages.
Nevertheless, during the sampling excursion to Corsica in summer 2018, we have observed that local stands of L. corsicum are very homogenous in morphological respects and exhibit only little variation in leaf dissection. Additionally, the retention of leaf-dissection characteristics of plants grown from seed from populations 434 and 436 (subsp. fenzlii) and population 439 (f. pinnatifidum) under common-garden conditions in the Botanical Gar- Fig. 3. Results of a Bayesian cluster analysis (STRUCTURE) of AFLP fingerprint data, with k = 2 found as the optimal cluster number. The histograms illustrate posterior probabilities of membership in the two clusters for each Leucanthemum accession under study. Letters below accession numbers refer to infraspecific taxa of L. corsicum (i. e. C: L. corsicum f.
Acknowledgements This work was funded by the grant of the  German Research Foundation (DFG)  in the frame of the SPP 1991 " Taxon-omics -New Approaches for Discovering and Naming Biodiversity " to C. O. ( OB 155/13-1 ) and  R. V. ( VO 1595/3-1 ). We would like to thank  Anja Heuschneider  for her most valuable technical help in the molecular laboratory of the  Evolutionary and Systematic Botany Group at Regensburg University. The technical help of the late  Monika Lüchow  from the  Botanical Museum Berlin  is gratefully acknowledged as well as the supportive work of  Jenny M. Köppe  and  Richard Pauwels  (both Berlin) with the determination of chromosome numbers. Salvatore Tomasello (Göttingen)  is thankfully acknowledged for the collection of plant material in 2011 used for karyological studies.
This study defines a monitoring method based on impedance analysis in the upper part of the spectrum. It focuses on the analysis of the high frequency behavior of electrical machines windings considering the turn-toturn capacitance variations (delta-C) due to insulation aging. An equivalent circuit is automatically generated from the turn arrangements in coils; parameters are computed considering the coil shape and the characteristics of insulation materials. PSpice analysis of the equivalent circuit yields the resonance frequencies to be monitored. With this software and a database giving the relationship between the turn-to-turn capacitance variation delta-C and the reduction of insulation performances due to aging, it is possible to build a monitoring system able to produce an alert when the probability of failure becomes higher than a predetermined level.
After forming the matrix corresponding to the circuit, the numerical calculations are executed. The result is a text output file "Netlist 2", its analysis can be made using the PSpice graphical interface. For a standard motor coil of approximately 50 turns, the equivalent circuit has several hundreds of elements; it becomes too complex for the graphical input interface of PSpice, which is replaced by another one written in MatLab environment. This interface builds the file "Netlist 1" from the relative positions of turns in a coil and the parameters of each turn. For a N-turn coil, the topology of the turns inside the coils is described by a NxN matrix made of Boolean elements that describes the adjacency of the turns. The lumped parame-ters determined for one turn are affected to the equivalent circuit using this matrix.
Duplication of Col10a1 Gene in Holocephalan and Elasmobranchs We searched the whole elephant shark genome assembly and identified five Col10a1 genes on scaffold 71, one Col8a1 gene on scaffold 42, and one Col8a2 gene on scaffold 121. Using these predicted coding sequences (CDS) to screen transcriptomic data of other chondrichthyan species led to the identification of six similar coding sequences in the catshark S. canicula and thornback ray Ra. clavata, as well as a single Col8a1 and Col8a2 sequence. In the transcriptome of little skate L. erinacea we have identified partial gene transcript sequences LSb2-ctg50203 for Col10a1. 1; LSb2-ctg2668069 for Col10a1. 2; LSb2-ctg41083 for Col10a1. 4; LSb2-ctg68055 for Col10a1. 5; LSb2-ctg688724 for Col10a1. 6; LSb2-ctg44808 for Col8a1; and LSb2-ctg1616013 for Col8a2.
In whale shark genome assembly we identified one of the hits, which was a long predicted transcript (XM_020514194. 1) spreading over 100 kb on the genomic locus NW_018064585. 1, to which four of the catshark cDNAs could be aligned in tandem (fig. 1  and supplementary table 1, Supplementary Material online). Two additional predicted CDS had a positive reciprocal BLAST best hit with catshark Col10a1 copies on two other genomic loci (fig. 1 ). Together, we identified six Col10a1, one Col8a1, and one Col8a2 gene sequences in the whale shark genome.
1 copy, not in the intron 6 of Nt5dc1 but in the intergenic space between the start of genes Frk and Nt5dc1 (fig. 1 ). Genomic data for osteichthyan species were extracted from public databases (Ensembl, NCBI, XenBase) and analyzed to reconstruct the synteny blocks of duplicated Col10a1 genes in chondrichthyans. Distances are to scale within each species but may differ between species. Abbreviation: ex, exon.
De Novo Transcriptome Assembly Sequenced reads of catshark vertebral column as well as publicly available reads of catshark (SRR5179116 and SRR5179117) and thornback ray (SRR5114772) lower jaw transcriptomes  (Irisarri et al. 2017)  were assembled as follows. Reads were trimmed using Trimmomatic 0. 36  (Bolger et al. 2014)  under the following parameters: ILLUMINACLIP: TruSeq-PE. fa: 2: 30: 10 LEADING: 3 TRAILING: 3 SLIDINGWINDOW: 4: 15 MINLEN: 20. All catshark sequencing reads from different sequencing experiments were pooled together and high quality paired-end reads for each species were assembled with Trinity  (Grabherr et al. 2011 ) using default parameters. Transcript expression levels were then estimated for each tissue using kallisto  (Bray et al. 2016 ).