dataesr/scientific-paragraphs-categorization huggingface dataset:
Overlapping feeding modes and prey type might be driving the apparent morphological similarities in overall skull shape and growth trajectory between Phocoena and some longirostrine oceanic dolphins, but these shared ecological traits do not explain the retention of low levels of skull asymmetry in adults and of other unique characteristics of their development. The unique echolocating abilities of Phocoenidae are the most likely limiting factor in the evolution of this group, and they influence multiple aspects of their ontogeny. The majority of the 75 extant species of odontocetes utilize broadband (BB) clicks in the 20-150 kHz for locating prey and communicating, and they present typically high levels of cranial asymmetry, as is the case in the oceanic dolphins present in this dataset and Delphinapterus.
The only trait that they all share is a relatively small body and skull size  [4] , which has been directly linked to the ability to hearing these high frequency sounds due to scaling of the melon  [31]. As Phocoena is the only NBHF taxon in the dataset, its unique ontogeny might be associated with the evolution of this specialized hearing frequency. A shortened and slower postnatal shape growth are the key developmental features that allow adult Phocoenidae to use NBHF sounds, allowing for the correct scaling of the melon relative to its body size  [31]. Retention of skull symmetry is likely a product of these other aspects of ontogeny, rather than necessary to develop the ability to use NBHF. This would also explain the asymmetric skull observed in some species of Lissodelphininae  [42]  and Kogiidae, one of the groups of Odontoceti with the highest skull asymmetry  [2, 13] , as well as the asymmetric soft tissue morphology of Pontoporia  [37, 41].
A medium degree of cranial asymmetry is retained in most odontocetes after this initial shift, with notable increases in Platanistidae, which include the modern South Asian river dolphin (Platanista gangentica), and in extant and extinct sperm whales (Physeteriidae and Kogiidae)  [2, 43]. These occurrences of higher asymmetry may signal the evolution of convergent ecological adaptations  [2, 4, 13, 41]  though achieved through different developmental routes, as it is the case in Delphinapterus and Globicephala in the present dataset. Qualitative studies of the ontogeny of Physeteriidae suggest that they present high levels of asymmetry from the early ontogenetic stages  [44] , providing additional support for the hypothesis that phylogeny influences the starting level and patterning of asymmetry in ontogeny.
Even in a dataset composed of relatively closely related taxa, developmental patterns display large variations. These changes can increment or negate each other through complex interactions in different aspects of development  [20]. Studying them is key to better understanding of adaptive patterns of evolution and distinguishing between traits that are directly related with functions and behaviours and those that are a remnant of the phylogenetic history of the clade  [16]. Investigating ontogenetic changes in Cetacea has already contributed to reformulate hypotheses on the evolutionary origin of some of their most distinctive traits such as hind limb loss [e. g. 16, 49] , teeth simplification and transition to baleen [e. g. 17, 50], ear bone morphology  [51] , and, here, skull asymmetry. The role of ontogeny in the evolution of unique morphological traits and ecological adaptations remains largely unexplored in many lineages, and we hope that similar approaches to one used here can be applied to future studies in the revived field of evo-devo.
Materials and Methods (a) Morphometric data The dataset is composed of 58 specimens representing all phases of growth of five odontocete genera from three families: Delphinapterus (beluga -Monodontidae), Phocoena (common porpoises -Phocoenidae), Globicephala (pilot whales -Delphinidae), Lagenorhynchus (whitebeaked dolphin, white-sided dolphins -Delphinidae), Stenella (spinner dolphins, spotted dolphins -Delphinidae). While some of these genera are comprised of multiple species, their morphological similarity and the large phylogenetic distance between genera allows us to use these genera as taxonomic units in the analysis  [24]  (electronic supplementary material, supplemental methods, dataset S1). Skull morphology was digitized using computed tomography (CT) scanning for fluid preserved specimens and disarticulated osteological specimens (electronic supplementary material, supplemental methods, dataset S2) and using a hand-held surface scanner (Creaform GoSCAN!) for whole osteological specimens.
3D rendering of CT data was performed in Avizo Lite. All meshes were imported in Geomagic Wrap 2017 for cleaning, reduced to 1. 5 million faces, and then exported as PLY for landmarking and visualization (electronic supplementary material, supplemental methods). To quantify the shape of the skull, 64 single points landmarks and 43 curves were placed on the surfaces using Stratovan Checkpoint. All points and curves were digitized manually for the right and left side of the cranium to capture asymmetry (electronic supplementary material, supplemental methods, figure  S8 , table  S7 ). The coordinates for each specimen were exported in PTS format to be analysed.
(b) Data analysis The shape coordinates for all specimens were imported in R  [52]  for data analysis. After resampling and sliding the curve semilandmark and fixing missing and absent points (electronic supplementary material, supplemental methods), the final dataset used for analysis is composed of 462 points, of which 64 are fixed landmarks and 398 are semilandmarks. Procrustes superimposition (GPA) was performed using the 'gpagen' function in 'geomorph' v. 4. 0. 1  [53]  to align and scale all specimens and extract Centroid Size (CS) to be used a measure of skull size in allometry analysis. Taxonomic and age information were separately imported to use as covariates. The specimens were binned into four growth categories based on their approximate age and total length: early fetus (from conception to <50% gestation), late fetus and neonate (from >50% gestation to birth, up to the first year of life), juvenile (after the first year until sexual maturity), adult (after sexual maturity) (electronic supplementary material, supplemental methods).
(i) Asymmetry analysis Asymmetry levels were assed only using fixed points landmarks, as using semilandmarks is not implemented consistently in the available methods  [2]. We used the function 'bilat. symmetry' from the 'geomorph' package to extract the Directional Asymmetric (DA) shape component and symmetric component of skull shape for each specimen. We then calculated the Euclidean distance between each of the symmetric shape landmarks and the DA shape landmarks was calculated with the 'linear. dist' function in the package 'landVR' v. 0. 4  [54]. We used these data to calculate mean distances for each landmark averaging specimens of the same taxon binned in the same growth stage, revealing information on where asymmetry is most visible in the skull. To compare the overall level of asymmetry in the skull across categories and taxa and identify significant differences, we used the 't.
test' and 'pairwise. t. test' functions from the 'stats' package v. 4. 1. 1 [52]. This also produced the mean distance for all the landmarks for each stage and taxon, which is useful to visualize how asymmetry varies in the dataset. As the interparietal bone is variably present in adult odontocetes  [18, 55] , we decided to remove the three landmarks marked on this bone from the asymmetry analysis presented in the main text (figure  1 ). However, we did perform the same full set of analyses on the whole skull configuration, and they produced comparable results (electronic supplementary material, supplemental methods, figure S9-S10-S11, table  S8 ).
(ii) Shape variation We assessed the variation of shape in the dataset using the whole configuration including semilandmarks by performing a PCA with the 'gm. prcomp' function (electronic supplementary material, supplemental methods) and a phenotypic trajectory analysis across the ontogeny of each taxon with 'trajectory. analysis' [19] , both implemented in 'geomorph'. The analysis of trajectory of shape change through ontogeny for each genus was performed on the raw shape coordinates. The function used also allows to significantly test pairwise differences between trajectories.
(iii) Allometry analysis As it has been proposed in other mammals [22], we first tested if the prenatal and postnatal allometries were significantly different from each other using the package 'mcp' v. 0. 3. 0 [56]  following  [23]. This package calculates the position of a breakpoint in a regression slope and allows us to test if the model with changing slopes is better at explaining the variation than a common slope model. We found that tested models with one break point were consistently preferred (electronic supplementary material, supplemental methods, figure  S12a-b , table  S4 ). Given the data binning chosen for the ontogenetic stages, we proceeded to create and test differences of allometry between taxa in the prenatal fetus and late fetus/neonate categories) and postnatal (juvenile and adult categories) ontogeny.
We used 'procD. lm' in 'geomorph' with specimens divided by genus and stage of growth (prenatal and postnatal) to reconstruct the allometric regressions, and we tested pairwise differences between slopes using 'pairwise' in the package 'RRPP' v. 1. 1. 2 [57]. We then extracted the regression parameters (slopes and intercepts) and used them to reconstruct ancestral allometric trends, a key step to assess the heterochronic processes at play  [20] , adapting the methodology presented in  [21]. Using a simplified phylogeny with branch lengths extracted from  [24] , we calculated ancestral slope and intercepts parameters for the nodes using the 'fastAnc' function in 'phytools' v. 1. 0  [58]. To assess significant changes in the allometric slopes between ancestral nodes and extant taxa, we used the package 'emmeans' v.
Figure1. Ontogeny of cranial asymmetry in toothed whales. Degree of asymmetry is quantified as the mean distance between the directional asymmetric (DA) and symmetric shape across all landmarks for all specimens of the same genus at each growth stage. The phylogeny drawn on the left highlights the close correspondence between phylogenetic structure and starting level of asymmetry in ontogeny. The skulls on the right represent variation in asymmetry in adult skulls. Landmarks in darker colours indicate more asymmetric regions, as they have a larger distance between the DA and symmetric shape relative to mean of all adults. Significant shifts in asymmetry levels between growth stages in the entire dataset and per genus are indicated by asterisks ('***' p<0. 001, '**'p<0. 01, '*' p<0. 05). See electronic supplementary material, tableS1, figure S1-S2 for full results.
Acknowledgments. Without natural history collections and curators, this study would have not been possible. We would like to thank  Brett Clark  from the  Natural History Museum, London, UK (NHMUK)  for the help with CT scanning specimens from the  NHMUK. For sharing CT data of specimens from the  Smithsonian Institution in Washington, D. C. (USNM) , we thank the  Michael McGowen  and  Janine Hinton.
Data accessibility. The surface files will be uploaded to the open access repository Phenome10K (https://www. phenome10k. org/) after publication. Landmarks, classifiers, and other data necessary for analyses, as well as all the code used, are uploaded to GitHub https://github. com/AgneseLan/ontogeny-asymmetry-dolphin (https://doi. org/10. 5281/zenodo. 6553285).
I. C and S-Fig. 3  in the Supplemental Material  [41] ). The latter have a much higher charge-to-mass ratio than heavier ions Z i /M i 1/m p , and can thus be quickly accelerated in the electrostatic field of this positively charged preplasma before its own Coulomb explosion. We have explored such interactions through 2D particle-in-cell (PIC) simulations using the code WarpX (v21. 04-86) [43]. The initial plasma density is uniform n p0 = 250 n c for z < 0, and for z > 0 it falls as n p = n p0 exp(-z/L g ), where L g = 8 nm (see  [41]  for details). Fig. 3 (a) presents distributions of the charge and electron densities modulated by the laser with FWHM duration τ las = 200 fs at t = -240 fs before the peak field arrival (t = 0). These modulations reach down to z ≈ 34 nm and the positive charge density reaches ρ ≈ en c (red colors in Fig.
This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreements 871124 and 694596 (LaserLab-Europe) and 694596 (ERC Advanced Grant ExCoMet), as well as from the Agence Nationale de la Recherche (ANR) under grant agreement ANR-10-LABX-0039-PALM. This research used the open-source particlein-cell code WarpX https://github. com/ECP-WarpX/ WarpX, primarily funded by the US DOE Exascale Computing Project. We acknowledge all WarpX contributors.
Results Our goal was to construct a biophysical model of a single neuron that can reproduce the different firing patterns recorded when extracellular potassium is increased, while keeping it sufficiently simple to allow a bifurcation analysis. The model is schematized in Fig. 1  (see methods section for the equations). It is a simplification of the classical Hodgkin-Huxley formalism, which also includes close surrounding environment with three compartments (external bath, extracellular space, intracellular space). The corresponding code is available on github at https:// github. com/ ddepa nn/ TheMo del. Numerous experiments show that seizures and SLEs are associated with an increase in [K] o  (Fisher et al. , 1976; Fröhlich et al. , 2008)  and that increasing of external [K] can trigger SLEs (S.
Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s10827-022-00811-1. Funding This research was partially supported by the European Union's Horizon 2020 research and innovation program under grant agreement No. 785907 (SGA2), and No. 945539 (SGA3) Human Brain Project and was also financed in part by the "Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001", the National Reasearch Council "Conselho Nacional de (  13 ) dV dt = i C m ( dΔ[K] i dt + dΔ[Na] i dt + dΔ[Cl] i dt ) (14) Δ[K] i + Δ[N ] i + Δ[Cl] i = C m W i V -V 0 (15) Δ[N ] i = -Δ[K] i (16) Δ[N ] o = -Δ[N ] i (17) Δ[K] o = -Δ[K] i (18) [K] i = [K] 0,i + Δ[K] i (19) [Na] i = [Na] 0,i + Δ[Na] i (20) [Na] o = [Na] 0,o + Δ[Na] o (21) [K] o = [K] 0,o + Δ[K] o + [K] g Desenvolvimento Científico e Tecnológico (CNPq)" and the Family Allowance Fund (CAF de Marseille, France).
Two airborne field campaigns focusing on observations of Arctic mixed-phase clouds and boundary layer processes and their role with respect to Arctic amplification have been carried out in spring 2019 and late summer 2020 over the Fram Strait northwest of Svalbard. The latter campaign was closely connected to the Multidisciplinary drifting Observatory for the Study of arctic Climate (MOSaiC) expedition. Comprehensive datasets of the cloudy Arctic atmosphere have been collected by operating remote sensing instruments, in-situ probes, instruments for the measurement of turbulent fluxes of energy and momentum, and dropsondes on board the AWI research aircraft Polar 5. In total, 24 flights with 111 flight hours have been performed over open ocean, the marginal sea ice zone, and sea ice. The datasets follow documented methods and quality assurance and are suited for studies on arctic mixedphase clouds and their transformation processes, for studies with a focus on arctic boundary layer processes, and for satellite validation applications.
Therefore, several airborne campaigns over the Arctic ocean have been conducted as part of (AC)  3  with either one or both of the AWI polar research aircraft Polar 5 and 6  15. The focus of these campaigns was on the observation of Arctic mixed-phase clouds and of the polar boundary layer in different seasons: the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD  [16] [17] [18]  ) in late spring and early summer 2017 based in Svalbard and the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) in spring 2018 out of Villum research station (Greenland). In this study we introduce and describe two follow-up campaigns that aim to extend the dataset, namely the Airborne measurements of radiative and turbulent FLUXes of energy and momentum in the Arctic boundary layer (AFLUX  19  ) in early spring 2019 and the MOSAiC Airborne observations in the Central Arctic (MOSAiC-ACA  20, 21  ) campaign in late summer 2020 which was the airborne component of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC  22  ) project.
Therefore, airborne measurements can fill an important gap to sense boundary layer processes and cloud development in this critical region. Thus, the general goal of the AFLUX and MOSAiC-ACA campaigns was to obtain a comprehensive dataset of atmospheric parameters in the polar cloud-covered and cloud-free atmospheric boundary layer (ABL) and lower troposphere over compact sea ice, the marginal sea ice zone, and open ocean. Research flights were planned in conjunction with atmospheric modeling such that they targeted specific conditions as, e. g. , cold air outbreaks. Specific flight patterns aimed to assess radiative and turbulent fluxes, thermodynamic profiles, and cloud macro-and microphysical properties. The combined analysis of the measurement data and modeling efforts set up for the observed cases can be used to estimate the role of Arctic clouds and surface heterogeneities for the amplified climate change in polar regions.
Furthermore, to get a grasp on the seasonal variability, a comparison of the observations from all campaigns that were carried out as part of (AC)  3  during episodes of several weeks in different seasons, is highly valuable. With the comprehensive dataset collected with the two campaigns, it is possible to perform various process studies and to evaluate satellite observations and atmospheric model simulations. For example, the data provide information on the life cycle of Arctic mixed-phase clouds, the partioniong into liquid and ice, the composition and amount of precipitation, or the impact of mixed-phase clouds on the radiation budget can be studied by these data. Furthermore, the dataset can be used to develop and improve model parameterizations and serve as critical test data to assess the performance of atmospheric models including the application of appropriate forward operators on the model output to mimic the measured quantities.
Flight strategies. The flight strategies can be grouped into three different measurement approaches: (1)   remote sensing of Arctic mixed-phase clouds and their transformation processes (e. g. , glaciation, precipitation formation, cloud dissipation); (2) in-situ probing of Arctic mixed-phase clouds; (3) measurements of turbulent fluxes of sensible heat and momentum over the ocean and sea ice. The resulting flight patterns designed to achieve these targets are illustrated in Fig. 3  and are described in the following. 1. Remote sensing measurements aimed to provide datasets for comparison with and as reference for satellite measurements, however, with much finer resolution. Flights were typically performed along straight legs over long distances at altitudes above 3000 m as needed for downward looking lidar measurements for eye safety reasons (Fig.
The vertical profile of basic meteorological variables have been provided by a dropsonde system. All instruments are described in more detail below along with their configuration for the campaigns. Table  2  summarizes the instruments used in both campaigns on Polar 5 along with corresponding parameters measured. Collections of the datasets have been compiled and can be found on the public database PANGAEA for both campaigns, AFLUX  26  and MOSAiC-ACA  27. A description of the file formats and the structure of the datasets is given in each instrument subsection and in the Data Records section. Nose boom and navigation system. The basic sensor of the nose boom installed on Polar 5, are an Aventech (Aventech Research Inc. , Canada) five-hole probe for high frequency pressure measurements, from which the wind vector can be derived that is placed at the tip of the nose boom and an open-wire Pt100 installed.
1 K), the humidity data were corrected for adiabatic heating and reach an accuracy of 2%  30. All data were recorded with a frequency of 100 Hz. However, it should be kept in mind, that the calibration of the 100 Hz data is only valid for straight and level flights, when using these for the calculation of turbulent fluxes. Turbulent fluxes are not provided in the dataset. This is left for the data user and can follow the examples given in  18, 31, 32. We stress that only such flight sections should be considered for the derivation of turbulent fluxes where the aircraft is flying in a straight line. Note also that most flights during MOSAiC-ACA and those during AFLUX over sea ice were carried out in conditions with absolute values of heat fluxes below 20 W m -2. Such conditions with low fluxes represent a challenge for the accuracy as compared to conditions with strong convection and strong signals  16.
Nevertheless, the comparison of flights with Polar 5 and Polar 6 during ACLOUD using the same nose boom equipment has shown a remarkable agreement of both measurement systems  16, 18. The processed datasets for the nose boom measurements are available in ascii format on PANGAEA in a 100 Hz resolution for thirteen flights of AFLUX  33  and ten of MOSAiC-ACA  34. Radar. The Microwave Radar/radiometer for Arctic Clouds (MiRAC; designed by Radiometer Physics GmbH, Germany)  35  has been designed for operation on board the polar research aircraft Polar 5 and 6. The active radar component (MiRAC-A) has been operated on Polar 5 on both campaigns AFLUX and MOSAiC-ACA. It consists of a single vertically polarized Frequency Modulated Continuous Wave (FMCW) cloud radar (RPG-FMCW-94-SP) at around 94 GHz and an additional horizontally polarized passive channel at 89 GHz using the same receiver for measuring the brightness temperature, that is used for the derivation of the liquid water path (LWP).
5 m close to the aircraft (up to 500 m distance) and 13. 5 m for the rest of the profile along the slanted path  35. The processed final datasets  37, 38  have a constant vertical resolution of 5 m with respect to nadir view underneath the aircraft. To achieve this, a multi-step post-processing is applied  35  that includes corrections and conversions of the signal: subtraction of mirror signal due to surface reflections, application of a speckle filter, correction for sensor altitude, mounting position, and pitch and roll angle  39  , and remapping onto the constant vertical grid of 5 m by taking into account each latitude and longitude position of each range bin. The temporal resolution is approximately 1 s which is the sum of the duration for both chirp sequences. Due to disturbances by surface reflections, the resulting regularly gridded data is only reliable from 150 m above ground level up to altitude of the aircraft.