dataesr/scientific-paragraphs-categorization huggingface dataset:
Structure of the ToNV polyhedrin The crystalline polyhedra is formed by repeating units of the ~27. 5 kDa ToNV polyhedrin protein, which is predominantly helical, containing ten helices and a short two-stranded antiparallel beta sheet (Fig. 1b , Supplementary Fig. 2a ). The polypeptide was resolved from residues 4-237 with only the internal loop 171-174 and short regions of each terminus insufficiently ordered to be modelled in the electron density map. The N-terminal 38 residues comprising helices-α 1-3 and an extended linker are separate from the body of the protein, forming contacts with adjacent molecules. The body of the polyhedrin molecule is formed by seven helices-α 4-10. Secondary structure homology searches using the FoldSeek, Dali, or PDBeFold servers against the experimental structures in the Protein Data Bank or predicted models in the AlphaFold Protein Structure Database did not reveal any structures homologous to ToNV polyhedrin  [26] [27] [28].
The largest single interface in the crystal  lattice is between the reference protomer and protomer XII. This interface is symmetric and is formed through domain swapping of the extended N-terminal 38 residues (Fig. 3b ), locking the sheets together. The interface buries 1268 Å 2 with residues from helices-α 1-3 swapping over to pack between helices-α 1-3 and helix-α 4 / helix-α 4 (Fig. 3b ). The strength of the lattice appears isotropic, with approximately 48% of the protomer interface buried (both hydrophobic, H-bond, and salt bridges) to form the 2D lattice which is further stabilised by at least four disulfide bonds. Between the sheets of the lattice, approximately 44% of the protomer interface is buried and further maintained by extensive domain swapping. In total each polyhedrin buries approximately 92% of the surface area when assembled into the polyhedra, a similar value to either baculovirus (for example PDB ID: 3JVB) or cypovirus (for example PDB ID: 2OH5) polyhedra at 96% or 98%, respectively.
A further two tyrosine residues are found in helix-α 2 and helix-α 3 , which participate in domain switching. Finally, eight other tyrosine residues are observed across the various protein interfaces. A change in the protonation state of these residues would likely effect the packing and stability of the lattice. During initial analysis of individual crystals of recombinantly expressed polyhedrin, we noted the presence of a crystal population (approximately 10% of crystals which could be indexed) where the unit cell had increased by 3. 5 Å in two directions, giving cell dimensions of a = b = 57. 2 Å, c = 105. 8 Å. These crystals diffracted weakly, yielding a dataset to approximately 2. 8 Å. Analysis of the structure showed significant portions of the structure were disordered. Residues 143-191 could not be resolved in the electron density maps, with helix-α 9 and the N-terminal region of helix-α 10 disordered (Supplementary Fig.
VMXm sample preparation Native and selenomethionine protein crystal datasets were collected on the VMXm beamline. The crystal solutions were first diluted with ethylene glycol to a final concentration of 50% (v/v). Grids were prepared in a GP2 (Leica-microsystems) at 20 °C and 90% humidity. All samples were applied to freshly glow discharged R2/2 Cu 200 (Quantifoil) mesh grids. To prepare the sample, 2 µL of crystal solution was applied to the carbon side of the grids and 2 µL 50% (v/v) ethylene glycol applied to the back side. The grids were then blotted from the back for 7 s before being plunged into liquid ethane  40. The grids were stored in liquid nitrogen until transfer into bespoke VMXm sample holders. Representative grids were imaged in a JEOL JSM-IT100 scanning electron microscope equipped with cryogenic sample stage and airlock (Quorum Technology).
Structure solution and determination The SeMet data were collected at the VMXm beamline at Diamond Light Source. All datasets were collected on a DECTRIS EIGER2 X 9 M CdTe and processed using DIALS v1 41 and xia2. multiplex v1  42. A singlewavelength anomalous diffraction experiment was carried out at 0. 9787 Å where 40°sweeps from 55 selenomethionine derivatised crystals were used after initial triage of 78 crystals. Exposures of 0. 02 s over a 0. 1°oscillation were collected using a 3. 5 μm × 3 μm beam. The structure was solved by CRANK v1. 5  43  which found 3 of the 4 selenomethionine sites that provided weak phases (FOM 0. 158, which converged to 0. 349 after density modification). These phases were good enough to automatically build a preliminary model (212 residues built with R/Rfree of 22.
3/29. 1). The structure was subsequently manually built in Coot  44  and refined in PHENIX  45. Figures were prepared using PyMol 2. 4. 1  46  and ChimeraX v1. 4-1. 6  47. Native data were collected on 65 of the 70-year-old crystals. The final dataset was composed of 16 data wedges, each with 200 images collected at 0. 6328 Å wavelength using a 3. 5 μm × 3 μm sized beam, with 0. 2 s exposure and 0. 1°oscillations. The SeMet protein model was used to phase the higher-resolution dataset.
Acknowledgements We thank  Helen Ginn ,  Geoff Sutton ,  Ervin Fodor ,  David Stuart , and members of  Grimes laboratory  for helpful comments and discussion. This work was supported by  Wellcome Investigator  Award  200835/Z/ 16/Z  (to J. M. G. ). We thank  Diamond Light Source  for access to the MX beamlines (proposal numbers  MX19946 ,  NT23570 ,  NT27314 ,.
Data availability All data are available from the corresponding authors and/or included in the paper or Supplementary information. Crystallographic data generated in this study have been deposited in the PDB including accession codes 8BBT (archival sample), 8BC5 (selenomethionine data), 8BCK (recombinant data), and 8BCL (recombinant dataexpanded cell). Data generated in previous studies (PDB ID: 3JVB and PDB ID: 2OH5 are available in the PDB. Source data are provided with this paper.
It remains unknown, however, if these subsurface eddies could imprint any signature on sea ice through dynamical or thermodynamical processes. Here, we aim at characterizing the (sub)mesoscale variability in the different layers of the Arctic Ocean, exploiting fully the 16-years long ITP data set completed with moorings from the Beaufort Gyre Observing System (BGOS). We seek observational-based evidences and a better understanding of the interplay between the Arctic dynamics at small scales and sea ice. The remainder of this paper is organized as follows. The various observational datasets used in our analysis are presented in Section 2. We then quantify the time and space variations of the energy at the (sub)mesoscale in the surface layer (Section 3) and in the subsurface layer (Section 4) and put them in regard with the variations in sea ice conditions.
Data As part of the Beaufort Gyre Observing System (BGOS, https://www2. whoi. edu/site/beaufortgyre;  Proshutinsky et al. , 2009) , up to four moorings have been deployed and maintained in the Canadian Basin since August 2003 (A, B, C, D, see Figure  1b ). At the time of the analysis, data were available until September 2018. An upward-looking Acoustic Doppler Current Profiler (ADCP) is mounted on the top of each mooring (except for mooring C), providing vertical profiles of horizontal ocean currents in the upper ∼30 m. Note that the ADCP was installed on mooring D in 2005, and in 2010 for moorings A and B. Profiles are returned every hour, from which we compute daily means, in order to filter the higher frequency fluctuations (likely induced by tides or inertial motions). We use the processed data interpolated on a 2-m fixed vertical grid.
In addition to the ADCP at the top, each mooring is equipped with a McLane Moored Profiler (MMP), which provides one-way vertical profiles of temperature, salinity and horizontal velocity between 50 and ∼2,000 m, separated in time by intervals of 6 and 48 hr, alternately. Again, we use the processed data provided with a 2-m vertical resolution. Finally, sea ice draft on top of each mooring is measured by an Upward Looking Sonar (ULS). The final product is daily and has an associated uncertainty of 5-10 cm  (Krishfield & Proshutinsky, 2006). In addition to the mooring observations, we also make use of observations collected by ITP, that are available through the ITP Program (http://www. whoi. edu/itp/,  Toole et al. , 2011 ). An ITP is similar to an Argo float, albeit drifting with the sea ice floe on which it is installed rather than ocean currents.
An ITP is composed of a surface buoy sitting on top of a sea ice floe, which supports a wire rope extending through sea ice and into the ocean, with a weight at the end to keep the wire vertical. The profiler climbs up and down along the wire and samples temperature, salinity, and pressure from ∼7 m down to 750 m, with a nominal 1 Hz sampling rate (corresponding roughly to a vertical resolution of 0. 25 m). Each ITP returns between 2 and 6 vertical profiles per day. In addition, some ITPs are equipped with a velocity sensor and return horizontal velocity (ITP-V;  Cole et al. , 2015). Between 2004 and 2019, 121 ITPs have been deployed over the Arctic Basin, providing ∼136,000 profiles (Figure  1a ), while 13 ITP-V provided 40,791 profiles including velocity (Figure  1b ). In the present analysis, we use data from 103 ITPs that were fully processed (Level 3) and provided on a 1-m vertical resolution grid.
The data from the additional ITPs are only processed to Level 2 and are provided on a 2-m vertical resolution grid. Throughout our analysis, we have checked that our results are not biased by the level of processing of our data set. In particular, we only use data processed at Level 3 in the spectra analysis presented in Sections 3 and 4. Data from the Thermosalinograph (TSG) mounted on the R/V POLARSTERN gathered during the PS101 expedition in the Nordic Seas in October 2016 are also examined  (Boetius & Rohardt, 2017). The transect used here is roughly a straight line in the Norwegian Sea (Figure  1c ). Temperature and salinity were measured at 11 m depth with a 10-min sampling interval. Last, we make use of the sea ice concentration data set distributed by the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSISAF, product OSI-401-b).
Regarding the sampling period of each instrument, a given ITP can provide data for one season only, or for both seasons. Some ITPs even cover two winters or two summers. Generally, ITPs drift faster 𝐴𝐴 (  ( 0. 1 𝑚𝑚 𝑚𝑚 -1 )) than the evolution of the Arctic mesoscale features, that are mostly advected by the weak background flow 𝐴𝐴 (  ( 0. 03 𝑚𝑚 𝑚𝑚 -1 )) on average at the mooring position, although this estimate may not be fully representative of the full basin where the ITPs are drifting). It means that the spectra analysis should allow us to examine the dynamics at mesoscale (length scales between R d and 100 km), following the methodology developed by  Timmermans et al. (2012)  and applied to a subset of our observational data set. We acknowledge, however, that the difference in speed is not an order of magnitude, meaning that the ITPs sample not only the spatial variability of the eddy field but also the temporal variability to some extent.
To exemplify a possible impact of sea ice on the surface dynamics, we further compare all our spectra corresponding to sea ice conditions found in the pack (Figure  3a ) to the spectrum obtained from the data of the PS101 cruise, that took place in the ice-free Norwegian Sea in October 2016 (Figure  3b ). We select a section of about 638 km performed over 18-20 October 2016 and interpolate the density field at 11 m depth on a 3-km resolution grid before performing the analysis, consistently with the processing of the ITPs (although we acknowledge that the transect was performed much faster than the drift of any ITP). Overall, the level of energy along this transect is similar to the level found under sea ice. The slope of the spectrum scales as k -2. 2 , similar to what can be found under sea ice, at least in summer.
Yet, this apparent seasonality, with more energy at smaller scales in summer is in stark contrast with what is found in the Makarov Basin. There, the slopes of the seasonally averaged spectra are very close (with a scaling of k -2. 6 and k -2. 9 in winter and summer, respectively), in line with the slope predicted from quasi-geostrophic theory. In this region, the sea ice conditions are most likely exhibiting little seasonality, while in the Canadian Basin, ITPs drift closer to the sea ice edge and might thus sample oceanic conditions forced at the surface by more diverse sea ice conditions depending on the season. When considering the Eurasian Basin, the ITP coverage is too sparse to obtain estimates for different seasons in all regions. Instead, we consider all the data available in the eastern side of the basin in winter, and obtain a very steep mean spectrum (with a slope scaling as -3.
Eddy Census We first use the full ITP, ITP-V and BGOS mooring datasets to perform an eddy census over the period 2004-2019, expanding significantly the latest census performed by  Zhao et al. (2016)  (up to 2015). Our eddy detection is largely done by visual inspection. It is primarily based on the method of  Zhao et al. (2014) , although, here, an eddy is characterized by both (a) an isopycnal displacement and (b) a temperature or a potential vorticity (PV) anomaly instead of solely an anomaly of temperature as in  Zhao et al. (2014). This allows us to detect eddies that are not associated with temperature anomaly in their core. PV is estimated as 𝐴𝐴 𝑓𝑓 0 𝜌𝜌 ( 𝜕𝜕𝜌𝜌 𝜕𝜕𝜕𝜕 ) with ρ the potential density and f 0 the Coriolis parameter, taken as the value at the North Pole (f 0 = 1. 45 × 10 -4 s -1 ).
Looking at the distribution of the eddy radius, there is a striking difference between the ITP-V data set on one side and mooring data set on the other side (Figure  8a ). While eddies detected from mooring observations have a large variety of radius, spanning from 3 to 80 km (with a peak in the distribution around 10 km), ITP-V only captures small eddies, with radius between 3 and 10 km. Most of the eddies detected by ITP-V are smaller than the deformation radius R d , and correspond to the SCV first identified in the central Beaufort Sea by D'Asaro (1988), although we note that the same structures were labeled as 'mesoscale eddies' by  Zhao et al. (2014). The difference found in radius logically translates into differences in vorticity and Rossby number (Figure  8b ). While moorings allow the detection of eddies with normalized vorticity ranging from -0.
1 to -1 (and Ro between 0. 05 and 0. 5), SCV sampled by ITP-V tend to be characterized by larger normalized vorticity and Rossby number. The differences in scales and properties of the structures captured by our two datasets appear to be robust, as the number of sampled eddies is significant. Yet, it remains puzzling as both observing systems are sampling the same region (Figure  1b ). Based on the analysis of mooring observations in the Eurasian side equipped with similar MMP and a similar sampling strategy (with profiles spaced by 6 and 48 hr, alternatively),  Pnyushkov et al. (2018)  found eddies with similar Rossby Number (Ro < 0. 1), suggesting that larger and weaker eddies are preferentially observed by moored instruments of this kind, which may not be suitable to capture smaller scale features.
, 2020) , by disturbing the stratification and modulating the local vertical heat flux (e. g. ,  Lique et al. , 2014). To quantify this impact, we use the ITP(-V) data set to estimate the mixed layer heat content as: with ρ 0 a reference density (1,027 kg m -3 ), C p the ocean specific heat capacity (3,985 J kg -1 K -1 ), (T ML -T f ) the difference between the mixed layer temperature T ML and the freezing point temperature in the mixed layer T f and dh the thickness of the mixed layer. The mixed layer depth is estimated based on a threshold criterion of 0. 1 kg m -3 (Peralta-Ferriz & Woodgate, 2015), compared against the first available measurements closest to the surface. In order to estimate the anomaly in mixed layer heat content associated with the passage of an eddy (ΔQ ML ), we assume that the average of Q ML at the two eddy edges represents the background Q ML , to which we subtract the value of Q ML at the eddy center.
The small impact on sea ice thickness might be due to the fact that the core of the majority of the eddies is close to the freezing point temperature, but also to the strong stratification at the base of the mixed layer that might prevent the transfer of heat upward, isolating sea ice from the thermodynamical impact of eddies. Although associated with errors, these calculations are the only way we could estimate of the thermodynamical impact of the eddy on sea ice. Indeed, some Ice Mass Balance (IMB) buoys were deployed on top of some ITP-V, providing direct in situ observation of sea ice thickness, but we could not find any data available when an eddy was detected. A few eddies stand out from our distribution, with value of ΔH ice reaching as high as 5 cm (Figure  9b ). We speculate that in specific conditions of stratification or eddy properties, the passage of eddies may contribute to the large spatial variations in sea ice bottom melt reported by  Perovich and Richter-Menge (2015).
Second, subsurface eddies could impact sea ice dynamically. It is well known that subsurface eddies can have a signature in sea surface height (SSH; e. g. ,  Chelton et al. , 2011) , as exemplified by the cases of meddies that have been associated to a local anomaly of 5-15 cm by  Bashmachnikov and Carton (2012). If significant, SSH anomalies (and associated geostrophic velocities and vorticity) resulting from the propagation of subsurface eddies in the Arctic could represent a significant forcing for the sea ice momentum balance, and thus modulate the sea ice drift  (Zhang et al. , 1999)  and vorticity  (Cassianides et al. , 2021 ). To quantify this potential impact, we compute the dynamic height (DH) at the surface following the relation: 𝐷𝐷𝐷𝐷 = ∫ 𝑝𝑝2 𝑝𝑝1 𝑑𝑑𝑝𝑝 𝜌𝜌 (4) with p the pressure, p 1 the pressure at the level of no motion chosen at 250 m depth (as most of the ITP-V do not have data below 250 m depth), and p 2 the pressure at the first available data point closest to the surface.
Although being the best choice possible given the availability of our data, we do acknowledge that the currents at 250 m in the Canadian Basin, where most of the ITP-V are drifting, are most likely non zero, and so our estimate of DH (and the quantities derived from it) may be somewhat biased. The sensitivity of our results to this choice is discussed below. Note also that the calculation could not be performed for eddy detected in the mooring data, as temperature and salinity data are not provided between above 50 m depth, preventing us from estimating dynamic height. For each eddy, we compute the gradient in DH at the surface between the eddy center and the eddy edge 𝐴𝐴 ( Δ𝐷𝐷𝐷𝐷 = 𝐷𝐷𝐷𝐷𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 -1 2 Σ𝐷𝐷𝐷𝐷𝑐𝑐𝑒𝑒𝑒𝑒𝑐𝑐 ) , and we compute the surface geostrophic velocity V g associated to this gradient as: 𝑉𝑉𝑔𝑔 = Δ𝐷𝐷𝐷𝐷 𝑓𝑓0𝑅𝑅𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 (5) The associated surface vorticity anomaly is further estimated as 𝐴𝐴 𝐴𝐴= 2𝑉𝑉 𝑔𝑔 𝑅𝑅 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒.
Discussion and Conclusion The dynamics of the Arctic Ocean at mesoscale and submesoscale remains poorly understood, despite their potential role for determining the sea ice conditions and their evolution. In this study, we present a detailed pan-Arctic characterization of the (sub)mesoscale variability over 16 years, making use of the large available datasets of in situ observations as extensively as possible. Based on a spectral analysis of the spatial potential density variance (which is equivalent to potential energy) sampled by ITP, we document a large variety of regimes that can be found under the sea ice pack. Indeed, both at the surface and at depth, one can find wavenumber spectra with slopes varying between -4 and -1 (Figure  3 ), regardless of the season, location or local sea ice condi tions.
This is likely because the subsurface layer is largely isolated from the surface by the strong peak of stratification at the base of the surface layer  (Meneghello et al. , 2021). Although we have tried our best to extract as much information as possible from the large observational data sets, it remains difficult to assemble a fully coherent picture of the small scale dynamics at the pan-Arctic scale. For instance, because ITPs are designed to sample the water column under thick sea ice, we are not able to perform the same diagnostics on potential energy in the regions that are seasonally ice-free. In the future, it would be interesting to examine data from surface and subsurface Lagrangian drifters sampling density continuously. Such analyses have been performed by  Mensa et al. (2018)  and  Timmermans and Winsor (2013) , but are thus far limited to very small datasets so that their representativeness might be questionable.
Acknowledgments This work was supported by the  French ANR  through the project  ImMEDIAT  (Grant  ANR-18-CE01-0010 ), and a PhD studentship from  Ifremer. G. M. was supported by  NASA  Grant  80NSSC21K0557. C. D. M. was supported by  NASA  Grant  80NSSC20K1140. We thank  Carolina Dufour  for a careful edit of the paper, and two anonymous reviewers for their constructive comments on the paper.
Data Availability Statement The BGOS mooring data were collected and made available by the Beaufort Gyre Exploration Program based at the Woods Hole Oceanographic Institution in collaboration with researchers from Fisheries and Oceans Canada at the Institute of Ocean Sciences. ITP data were collected and made available by the ITP Program  (Krishfield & Proshutinsky, 2006; Toole et al. , 2011)  based at the Woods Hole Oceanographic Institution. The datasets are publicly available in post-processed form at https://www. whoi. edu/beaufortgyre and http:// www. whoi. edu/itp. Data from the Thermosalinograph (TSG) are available at https://doi. pangaea. de/10. 1594/ PANGAEA. 873145. The sea ice concentration from OSISAF, product OSI-401-b  (Tonboe et al. , 2017) , is available at https://osi-saf.
000) that were digitized in a GIS (ArcGIS 10. 7. 1 Esri Inc. ). The others 95 old plots were extracted from a regional floristic database (https://www. biodiversiteauvergne-rhone-alpes. fr/pifh) for which geographical coordinates in latitude and longitude, were provided. We then used a series of aerial photographs, taken from 1979 to 1986 for old series, and from 2015 to 2020 for recent series, to eliminate old survey plots where significant land use change occurred. The final dataset consisted of 118 old survey plots distributed throughout the study area (Fig. 1 ) and representing main riparian habitats. These old plots, although not permanently marked, had a reliable approximate location and can thus be considered "quasi-permanent" plots  (Kapfer et al. , 2017). Based on the locations translated into geographic coordinates of old survey plots, recent survey plots were carried out in late spring (end of May to June) in 2020 and 2021 using the same methodology.
They were relocated using a centimetric-accurate GPS receiver (©GNSS Trimble R2). For each recent plot, plants communities were sampled in quadrats of comparable size to the original old plots, i. e. , from 50-100 m 2 for herbaceous vegetation to 400-800 m 2 for forest stands; and plant species cover was recorded using the Braun-Blanquet method. However, because species cover data were not available for the old plots, subsequent analyses were performed considering presence/absence data. Finally, to limit observer biases, all recent surveys were performed by the first author (VB).