dataesr/scientific-paragraphs-categorization huggingface dataset:
Yet, whereas the literature is rich for some species, it is scarce for others. Moreover, the functional relevance of the categories we used remains to be tested using feeding performance measurements or models. The phylogenetic signal in our dataset is <1, suggesting that there is more variation in the head shape of snakes than expected under a pure Brownian Motion model of evolution  (Adams, 2014a). The biological significance of K mult lower than 1 has been of interest to the scientific community. Early hypotheses suggested it could be the result of selection, adaptation, or measurement error  (Blomberg et al. , 2003). A recent review by  Adams and Collyer (2019)  suggests that K mult < 1 might be obtained when the phylogenetic signal is concentrated in a subset of traits. Yet, to our knowledge, no procedure to test for this hypothesis is currently available  (Adams & Collyer, 2019).
We measured the hydrodynamic constraints that resist the forward attack of a snake under water. The higher the constrains, the higher energetic cost for the animal to move its head forward  (Vogel, 1994)  (Equation  4 ). Thus, we expect a selective regime to favor hydrodynamically efficient shapes, which, in a context of morphological disparity, could be explained by a many-to-one mapping of form to function. The range of drag coefficients we found are consistent with previous simulations that have been performed using a 3D scan of the head of Natrix tessellata coupled to in vivo data on striking  (Van Wassenbergh et al. , 2010). These simulations Our results are also consistent with the drag, and added mass coefficients found in the literature for prolate spheroids  (Vogel, 1994). Drag coefficient has been calculated for a variety of other aquatic animals such as invertebrates  (Alexander, 1990; Chamberlain & Westermann, 1976) , fish  (Webb, 1975) , amphibians  (Gal & Blake, 1988) , turtles  (Stayton, 2019) , birds  (Nachtigall & Bilo, 1980) , and mammals  (Fish, 1993 (Fish, , 2000)) ); yet, they are not directly comparable, as different reference areas (i.
The long and thin head with larger eyes might allow them to have a larger binocular field of vision and thus to be able to target their prey more accurately, whereas more aquatic snakes might not primarily rely on visual cues and thus show a reduced eye size  (Hibbitts & Fitzgerald, 2005). Overall, the main axis of variation in our dataset seems to follow a trend from fully aquatic species with bulky heads grouping around the maxima of the main components (PCmax) and the more terrestrial species, with slender heads, on the minima (PCmin). Quantitative ecological data are needed in order to properly test for the proportion of time a species spends in water on its head shape. This pattern suggests a competition between the different functions of the head leading some species to evolve in opposite directions of the morphospace to Future work should include the development of feeding models in order to measure performance related to food acquisition and swallowing in snakes, as feeding is probably the more constrained and fitness-relevant activity of a snakes' head.
GEUS data The sampling areas were chosen due to known mineralization and exploration in the area. The interest is in the area Isortoq, which is situated in the very south of Greenland, see Figure  1  for a detailed map, where the map background is obtained using Google maps  (Kahle and Wickham 2013). In total, three traverses were sampled which are 300 meters apart. The samples from the different traverses are shown in different color in the map. Green color refers to the locations of known mineralizations. In this case the deposit is an Iron (Fe), Vanadium (V), Titanium (Ti) deposit. A possible proxy for V could be Scandium (Sc) (since V tends to be analyzed poorly). In our analyses we merge the samples from the three traverses into one linear transect, which means that all samples have been taken, but their locations are set to a linear transect in the center of the three traverses.
Acknowledgments The authors acknowledge support from the  UpDeep  project (Upscaling deep buried geochemical exploration techniques into European business, 2017-2020), funded by the  European Information and Technology Raw Materials. We thank GTK personnel  Janne Kivilompolo  and  Kari Kivilompolo  for mountain birch sampling and  Jukka Konnunaho  for organizing the funding and project  management of Mineral potential of northern Finland.
Exposures to chemicals In the present study, glyphosate acid (97% purity, CAS number: 1071-83-6) and the POEA mixture were obtained from Dr. Ehrenstorfer GmbH ® (Augsburg, Germany) whereas Roundup Express ® (REX) was purchased from a garden centre. For REX, all the concentrations given in this study were expressed in glyphosate equivalents. All solutions of chemical compounds used were prepared with sterile Hank's M199 medium. Three different endpoints were studied: viability (MTT assay), lysosomal stability (neutral red retention assay) and phagocytic activities (fluorescent beads). The tested concentrations are provided as supplementary data (S1 Table ). For the MTT assay, all the concentrations (from 0. 1 to 100,000 µg L -1 ) were tested at least with the cells of four abalones and in triplicate (N ≥ 12).
Statistical analyses As the data from MTT assay and neutral red retention do not meet the assumption of normality and homoscedasticity for an ANOVA, these data were analyzed using non-parametric Kruskal-Wallis (K-W) tests (k > 2) or Mann-Whitney tests (k = 2) for independent samples. In case of H0 rejection after a K-W test, post-hoc Dunn tests were used in order to detect differences among the different concentrations. The data for phagocytic activities and NRRA fulfilled requirements for analysis by one way ANOVAs followed by a posteriori Student-Newman-Keuls (SNK) tests or t-tests (k = 2). The statistical analyses were performed using STATISTICA 8. 0 software (Statsoft ® , Tulsa, OK, USA). EC50 values were computed with nonlinear regressions (Hill equation) using Excel® macro REGTOX  [44].
These data allowed the ANSPs to release special aviation advisories (e. g. Significant Meteorological Information -SIGMETS). The impact of the event was recorded by keeping track of all aspects related to economically guided decision making in airline operational centres. Further impacts were due to rerouting and cancellation of flights by flight trajectory modellers (see section 5. 3) as well as respective procedures invoked on the military side and participating airports and ministries. For the evaluation of the impacts on the air traffic, the air traffic was simulated by using the NAVSIM  (Rokitansky, 2009)  simulator and was analysed by using Key Performance Indicators (KPIs). A newly developed cost model was used in the framework of an airline network balance tool (Flightkeys 5D) to costefficiently re-route flights affected by the disaster event (see section 5.
Simulations of the artificial plume evolution Having the parameters of the artificial eruption decided, we have run the SILAM modelling system of FMI (http://silam. fmi. fi, Sofiev et al, 2015)  over the European domain simulating the dispersion of the emitted masses in the atmosphere (see example in Fig. 5 , left). The main challenge at this stage was to simulate not only the main dispersion but also account for incompleteness of our knowledge of the atmosphere, its dynamics during these days, actual wind direction and speed, etc. To account for this uncertainty, the SILAM model was run with a realistic meteorology during the selected period but the fields were taken not from the operational weather models but rather from the reanalysis dataset ERA-5 of European Centre of Medium-Range Weather Forecast (ECMWF).
Synthetic EARLINET/ACTRIS data The European Aerosol Research Lidar Network (EARLINET;  Pappalardo et al. , 2014; www. earlinet. org) , established in 2000, provides aerosol profiling data on a continental scale. EARLINET is part of the Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS; www. actris. eu). The main EARLINET products are the aerosol backscatter and extinction profiles. The data allowed investigating several volcanic eruptions in the last 20 years (e. g. Etna 2002 eruption; e. g. Pappalardo et al. , 2004; and Eyjafjallajökull volcano in Iceland Pappalardo et al. , 2013). Within the EUNADICS-AV project, an EWS was designed that relies explicitly on EARLINET aerosol observations. This product is based on the possibilities offered by the EARLINET Single Calculus Chain (SCC; D'  Amico et al.
, 2015)  for the NRT data processing and the generation of tailored products network-wide. The calibrated high resolution lidar data serve as the basis for the alert delivery  (Baars et al. , 2017). However, the information reported in the EWS provides only qualitative and not quantitative information thus the EARLINET EWS represents a warning system rather than a tool for decision makers. The demonstration exercise was the first occasion in which the EARLINET EWS was tested. The EARLINET products for the demonstration exercise consisted of snap-shots of the lidar signals and of the EWS plot. Figure  6  shows an example of the particle backscatter coefficient (with the cloud fraction removed) for the EARLINET station in Barcelona. Special attention should be given to the particle backscatter coefficient values reported in the figures as the values are unrealistically high and most likely would attenuate the lidar laser beam.
Synthetic satellite data simulated for IASI and MODIS Satellite observations from 2 types of sensors (IASI and MODIS) were considered for generating alert products for the exercise. Clerbaux et al. (2009)  and  Levy et al. (2015)  gave descriptions of the IASI and MODIS instruments respectively. Synthetic ash products from two IASI sensors (onboard MetOp-A and MetOp-B;  Clarisse et al. , 2010)  and aerosol optical depth (AOD) from two MODIS sensors (onboard Aqua and Terra) were used. After the detection of the aerosol/ash cloud at the Nicolosi and Catania EARLINET stations, the first selective detection of ash from satellite was created for IASI-A (Fig. 8 , left) few ash pixels at 20:10 UTC on April 18, 2018). A first partial detection was created for 08:28 UTC on April 19, 2018, followed by a global detection of the ash plume by IASI-B (Fig.
Using this approach was a slightly conservative compromise. Figure  9  shows the dispersion of the volcanic ash cloud over several days at a selected layer (FL275). Note that the model data were produced in advance of the demonstration exercise. The modelled results were used as baseline for the aviation related tasks. The data was interpolated to 13 flight levels, visualized on the EUNADICS-AV portal and also imported into other applications like flight planning software and visual weather (https://www. iblsoft. com/products/visualweather/). At this demonstration exercise, the tasks designated to ANSP's during such a crisis situation were executed by Austro Control (ACG). Aviation weather services provided specially tailored warnings and/or products for different aviation stakeholders in order to warn about the presence of the volcanic ash cloud.
During the course of the demonstration exercise, the datasets described in the previous sections were fed into the visualisation software that is also used for daily operations. Subsequently, products were generated for the local situation in Austria in order to give the exercise participants an impression of available aviation products for the case of a volcanic ash eruption. The presented products included internationally prescribed and harmonized weather warnings or prediction products, as well as country specific products for Austria. In addition, a pilot briefing was conducted including specific information on the volcanic ash event for a potentially affected flight. The following internationally harmonized products (selected examples) were prepared (according to an ACG internal guideline): • Volcanic ash SIGMET (Significant Meteorological Information) A SIGMET information is a warning message of the occurrence or expected occurrence of specified en-route weather phenomena, which may affect the safety of aircraft operations  (ICAO, 2018a).
Acknowledgements This work has been conducted within the framework of the  EUNADICS-AV  project, which has received funding from the  European Union 's  Horizon 2020 research programme for Societal  challenges -smart, green and integrated transport under grant agreement no. 723986. We thank the Austrian military for hosting the exercise and providing technical equipment together with the  University of Salzburg. https://doi. org/10. 5194/nhess-2019-265Preprint. Discussion started: 12 August 2019 c Author(s)  2019. CC BY 4. 0 License.
HCl pH 8. 0, 150 mM NaCl) with 2 ml reservoir solution 0. 4M NH 4 I, 26% (w/ v) PEG 3350, 0. 05M MES pH 6. 5, 21% glycerol. Initial anomalous signal was detectable only to 4. 5 A ˚, improved signal was achieved by quick-soaking crystals in 0. 1M 'magic triangle' I3C  (Beck et al. , 2008)  for 20 s. Highly redundant data sets were processed using XDS  (Kabsch, 2010)  and scaled with Aimless  (Evans, 2011). The structure was solved by SAD, exploiting the iodine atoms as anomalous scatterers, the iodine sub-structure was solved with ShelxD  (Schneider and Sheldrick, 2002)  and refined with Sharp  (Bricogne et al. , 2003). Density modification was performed with Pirate and resulting electron density maps allowed for manual chain tracing and initial model refinement with Buster. A second dataset diffracting X-rays to higher resolution was eventually used for final refinement with Buster, iterated with manual rebuilding and validation with Coot.
The addModPts program in PEET  (Cope et al. , 2011)  was used to set the repeat spacing between each particle point to 52 A ˚. Using the IMOD model2point program, the particle model file was converted to a text file format that designated X, Y, and Z coordinates for each filament and assigned an incrementing number for each individual filament for import into RELION version 2. 1. b1-gcccuda-2016. 10-cc37 (Scheres, 2012). The RELION  (Scheres, 2012)  sub-tomogram averaging module  (Bharat et al. , 2015)  was used for initial sub-tomogram alignment. EmClarity  (Himes and Zhang, 2018)  was for subsequent high-resolution refinement and 3D reconstruction, and an in-house method for smoothing and interpolation of sub-volume coordinates  (Huehn et al. , 2018 ; Figure  2 -figure supplement 2). A variation of the RELION pre-processing PYTHON script  (Bharat et al.
Efforts to use the RELION subvolume average as a search template for the emClarity  (Himes and Zhang, 2018)  particle picking procedure failed to yield useful particle coordinates. In-house scripts were therefore utilized to export the RELION alignment parameters to emClarity. Following coordinate import, CTF estimation and correction was performed in emClarity. The optimal defocus estimate was set to À3. 0 mm and the defocus window was 0. 5 mm. For averaging and alignment steps in emClarity, particle dimensions and box sizes, as well as inbuilt shape masks were specified in the emClarity parameter file. Following initial estimates for a filament diameter of ~240 A ˚and a repeat spacing of 52 A ˚, the particle 'radius' was set to (145 A ˚, 145 A ˚, 26 A ˚) in the parameter file (param0. m).
A rectangular alignment mask with x, y, z dimensions of 200, 200, and 160 A ˚was applied, encompassing 6-8 repeats. Anisotropic SSNR calculation was activated (flgCones = 1). To avoid FSC overestimation due to mixing of closely packed particles together between half datasets, the fscGoldSplitOnTomos option was also activated. Iterative averaging and alignment steps were run in emClarity for 3-5 cycles with increasingly restrictive search angles and translational shifts. Particle coordinates and Euler angles from several cycles (1, 3 and 5) were extracted from the emClarity results file for analysis of individual filaments. Areas in the tomograms where filaments had lower SNR with respect to the background solvent had poorer tracking of particle coordinates along the filament trajectory, leading to 'breaks' and translational shifts in the x,y,z coordinates or Euler angle misalignments.
For each filament, particle coordinates were extracted and graphed using in-house scripts. Particle outliers were deleted, and an inhouse script was used to smooth a trajectory through the remaining coordinates, and to fill in gaps in the smoothed trajectory with particles according to a repeat spacing of 52 A ˚. The particle assessment, excision, and trajectory smoothing procedures were performed several times until the particle coordinates followed a regular, smoothed trajectory. These final coordinates were then re-imported into emClarity for a final 8 cycles of averaging and alignment at a binning of 2 for cycles 0 through 3, and a binning of 1 for cycles 4 through 8. The angular search range (rawAngle-Search=1 st ,2 nd ,3 rd ,4 th ,5 th ) can be set for each alignment cycle with the first two values corresponding to the angular range and increment of the out-of-plane search.
The 3 rd and 4 th values correspond to the angular range and increment for the in-plane search. The 5 th value was set to 0 to turn off the helical search, which was not effective for our system. At a binning of 2, the rawAngleSearch range for cycles zero through three, in order, were (0,0,7,1,0), (4,1,0,0,0), (0,0,3,0. 75,0), and (2,1,0,0,0). For cycles four through eight, at a binning of 1, the rawAngleSearch ranges were (0,0,5,1,0), (3,1,0,0,0), (0,0,3,0. 75,0), (2. 25,0. 75,0,0,0), and (0,0,1. 5,0. 5,0). The two half-dataset volumes were combined with a B-factor of 0, and the Gold standard FSC at 0. 143 was calculated to be 9. 83 A with anisotropic resolutions ranging from 8. 89 to 16. 17. L. biflexa fcpA -and fcpB -flagella. A similar sub-tomogram averaging procedure as described above was followed for the fcpA -and fcpB -flagella tomograms.
Specifically, IMOD was used for motion correction (using the program alignframes) and the tilt series were locally aligned through use of the fiduciary markers. CTF correction was also performed in IMOD through phase flipping, followed by subtraction of the fiduciary markers from the tomograms. The final aligned tomograms were binned by 2, resulting in a pixel size of 4. 97 A ˚. Tomograms were then reconstructed in Tomo3D using weighted back projection, and 3dmod (IMOD) was used to select the filaments. The addModPts program in PEET was used to add points corresponding to the repeat spacing of 52 A ˚, and the IMOD program model2point was used to prepare the files for import into RELION. The fcpB -tomograms were imported into RELION using the same scripts as described above, with a box size of 480 A ˚.
A total of 24 tomograms were imported, corresponding to 5917 particles. In the program 3dautorefine, no initial reference model was used, and the symmetry was C1, the particle mask was 470 A ˚, and the same angular search parameters as for the wild-type structure were used. The resultant fcpB -structure had a resolution of 26. 5 A ˚, and similar to the wild-type reconstruction, appeared to have large missing wedge artifacts. For fcpA -, a total of 25 tomograms were used, yielding 4327 particles; particles were imported into RELION and refined using similar parameters. Due to limited signal quality and/or sample heterogeneity, the fcpA -refinement did not yield a converged structure, with inconsistent particle axial rotations along single filaments. Nevertheless, the resulting fcpA -subunit x, y, z coordinates yielded continuous trajectories for many of the filaments.
Using 2D classification, two distinct fcpA -particle diameters were identified, with 2958 particles in a smaller-diameter class (~120 A ˚) and 323 particles in a larger-diameter class (~170 A ˚). Particle coordinates from these two classes were used for subsequent trajectory/curvature analysis. For the fcpB -dataset, the RELION-refined x, y, z coordinates and Euler angles of all 24 tomograms and 5917 particles were imported into emClarity, using the same in-house scripts as described above. CTF correction and estimation was performed, with an initial defocus estimate and window of À5. 0 mm and 2. 0 mm respectively. Due to reduced signal to noise ratio in the fcpB -data, the reference alignment procedure failed to unambiguously establish the polarity of many of the filaments, which were therefore discarded.
After this analysis, 1163 particles corresponding to 14 filaments from 10 tomograms remained. Initial emClarity parameters designated the particle radius in x, y, and z as 150, 150, and 20 A ˚, and applied a rectangular alignment mask of 180,180, 250 A ˚. As in the wild-type dataset, the flgCones and fscGoldSplitonTomos options were activated. Five cycles of averaging and alignment were used, in a manner similar to that described above, with each cycle using a binning of 2. The rawAngleSearch range for the cycles, in order, were (0,0,7,1,0), (5,1,0,0,0), (0,0,5,1,0), (4,1,0,0,0), (0,0,3,0. 75,0), and (0. 75,3,0,0,0). After the fifth cycle, the half data sets were combined, giving an average FSC of 18. 41 A ˚, with a range from 14. 58 A ˚to 31. 67 A ˚at a B-factor of 0.
Resolution estimation Initial resolution estimates of final subtomogram average volumes were estimated using emClarity as described  (Himes and Zhang, 2018 ; Figure  2 -figure supplement 2). For additional resolution estimates, emClarity source code was modified to output unmasked copies of both refined half-dataset volumes, and these volumes were input to other software packages: local resolution estimates (Figure 2-figure supplement 4) were made using 'Relion version 3. 0. 6 (Zivanov et al. , 2018) , and direction-dependent resolution estimates (Figure  2 -figure supplement 5) were made using 3D FSC  (Tan et al. , 2017). For local resolution estimates, maps were sampled at a rate of 40 A ˚(Relion option '-locres_sampling 40'), with local regions defined by a spherical mask of radius 20 A ˚softened by a cosine edge function of (40 A ˚falloff).
, 2004)  module Segger. To generate a 'triplex' of FcpA or FcpB atomic coordinates, the highest-scoring initial hit was axially shifted by one 52 A ˚helical repeat forward and backwards to generate a composite PDB file containing three FcpA (or FcpB) monomers at consecutive sites along a single protofilament. Top-scoring hits from this second round of SITUS searching identified four additional protofilaments of FcpA, for a total of 6 protofilaments, and three additional protofilaments of FcpB for a total four protofilaments. The relative positioning of FcpA and FcpB in our sheath model was further supported by an independent computational docking strategy, in which we utilized pre-formed FcpA/FcpB dimers predicted by computational methods. In order to construct an FcpA:FcpB complex, we first modeled L.
Estimating supercoil parameters from subtomograms For each subtomogram average volume (wild-type or fcpB -), the volume was superposed on a 53 A åxially-shifted copy of itself using the UCSF Chimera command 'Fit in Map'. Parameters for the superhelical rotation axis were then estimated using the Chimera command 'measure rotation'. The superhelical diameter was estimated as twice the distance from the superhelical axis to the filament central channel, while the superhelical pitch was estimated as the reported volume shift along the superhelical axis multiplied by 360˚divided by the reported volume rotation. The following previously published datasets were used:.
Acknowledgements We gratefully acknowledge  Garrett Debs  for help with the RELION subtomogram averaging preprocessing step, including his customizations of published python scripts for Relion sub-tomogram averaging. We thank the  IT Department from Institut Pasteur  for protein modeling computations on the TARS cluster and the staff in the  Yale High-Performance Computing facility  for their maintenance of these facilities. We thank  Prof. Felix Rey  for providing insightful discussions about the manuscript.