dataesr/scientific-paragraphs-categorization huggingface dataset:
Although the three methodologies agreed on classifying this sequence as a putative Gamma PV, the two alignment methods did not perfectly align the putative new PV sequence (less than 85% similarity against known PVs). Among the 39 putative known PV sequences identified in this sample pool, one was represented by about 7% of the total reads (4211 raw reads out of 60,892 reads) (Table  2 ; Supplementary Table  S6 : Sequence identifier "69VIRUSput"). The MegaBlast algorithm aligned this sequence to a partial cds (342 bp) of a major capsid protein L1 gene (isolate GC12_1; FJ969907. 1) with nearly 99% of identity. In comparison, the BlastN alignment against the PaVE database aligned this sequence against a Gamma-10 referenced PV genome (HPV130; GU117630), with a percentage of identity below 10% (86.
Beta-3 species were the most represented species identified by the three methods, with 42% of beta-3related sequences identified by MegaBlast, and 62% identified by both BlastN and RaxML-EPA (Supplementary Tables  S2, S3 , and S4). The second most represented group was the "unclassified" sequences for MegaBlast (28% of the sequences), due to the incomplete taxonomic classification of a proportion of Papillomaviridae-related sequences present in the NCBI database. The third most represented genus based on MegaBlast was the gamma genus, with about 24% of the sequence, followed by the alpha genus (2%) and a small proportion of Lambdapapillomavirus (0. 03%) due to the identification of a feline PV partial cds sequence (EF535004. 1) in sample pools 1 and 2 (Supplementary Tables  S2  and S6 ). The second most represented group based on BlastN and RaxML-EPA was the unreferenced PVs, with a major subset putatively classified as unreferenced Gammapapillomavirus sequences (about 17%) and a small subset as unreferenced Betapapillomavirus sequences (about 1%).
Of note, unreferenced sequences represented about 40% of the total entries available in the PaVE database version used (version of  May 23, 2019). The third and fourth most represented genera were the referenced gamma and alpha PVs by both BlastN and RaxML-EPA (Figs. 3  and 4 ). BlastN could not classify 0. 008% of the sequences, due to a best subject sequence associated with an e-value under the threshold defined as 1e-1 (Supplementary Table  S3 ). RaxML-EPA also classified 0. 8% of the sequences as "Unclassified" because those sequences presented homology to a newly described Erethizon dorsatum PV (EdPV2; MH376689), not yet classified by the ICTV, and potentially the first representative genome of a new PV genus  [22]. Interestingly, the 46 reads that were unclassified by BlastN (due to the e-value threshold) were classified as Taupapillomavirus by RaxML-EPA, with homology to Felis catus PV type 4 and 5 (Supplementary Table  S4 ).
Discovery and characterization of putative new PV-related sequences Overall, from the entire run, a total of 3 putative new sequences belonging to the Papillomaviridae family were identified by the algorithm (Table  3 ). Based on MegaBlast, "PV_1" is close to an unreferenced Gamma-12 complete genome, also present in the PaVE database (MF588716). However, it shows a higher percentage of identity with HPV-mSK197 (MH777339) based on BlastN alignment against the PaVE database. RaxML-EPA was in agreement with BlastN results, finding homology with the unreferenced Gammapapillomavirus HPV-mSK197. "PV_2" presented similarity (based on MegaBlast) with an unreferenced Gamma-13 complete genome (MF588722), which is absent from the PaVE database. BlastN found similarity with HPV-mEV03c45 (MF588721), an unreferenced Gammapapillomavirus genome, and RaxML-EPA found homology to HPV213 (MF509818), a referenced but unofficially classified Gammapapillomavirus genome.
In the current analysis, this sequence appeared in the putative known PV sequence, because it is now included in the NCBI database (MF356498. 1) as well as in the PaVE database. However, this sequence is still assigned to an unclassified group by the BlastN algorithm because the taxonomy has not yet been updated in the PaVE database (Supplementary Table  S6 , "37VIRUSput"). The official number of this novel PV, named "HPV isolate ICB1" in the NCBI database, is HPV224.
Performances The PVAmpliconFinder execution time on this dataset was less than 150 min when using an indexed NCBI database (Table  4 ). The most time-consuming step was the MegaBlast search against the full "nt" NCBI database (more than 95% of total time). When using a non-indexed NCBI "nt" database, the MegaBlast computational time was reduced to less than 5 min (Supplementary Table  S7 ). For most of the steps, parallelization at the sample level was implemented to reduce the total computation time.
Discussion We developed PVAmpliconFinder, a complete workflow enabling the discovery and identification of viral sequences related to the Papillomaviridae family from targeted amplicon sequencing by NGS. PVAmpliconFinder is an easy single-line command workflow that takes FastQ files as input files and generates tabular and graphical output files that describe the nature and abundance of PV-related sequences present in a complex mixture of host, phage, bacterial, and viral DNA. The data output discriminates between putative new and previously known Papillomaviridae-related sequences. Furthermore, it includes sequencing metrics and sequence details, enabling the design of subsequent laboratory experiments for confirming the in silico findings (Supplementary Data 3). In contrast to read-subtraction methods, PVAmpliconFinder performs an alignment step against the entire NCBI database.
This is a deliberate choice because removing host sequences may remove potentially new viral sequences that present some similarity to the host. Indeed, viruses are the fastest mutating DNA element on Earth  [24] , so the chance of random sequence similarity between a large host genome and a small viral sequence is high. Moreover, the use of degenerate primer leads to the amplification of more diverse pieces of DNA and finding the best match against a Papillomaviridae sequence when aligning against a multi-organism database provides more robust results. Several steps of the workflow are specifically tailored to deal with the specificity of NGS amplicon sequencing: the merging of the read pairs, enabling the reconstruction of the full insert; the de-replication step, to reduce data complexity and keep only one copy of identical sequences; and the elimination of chimeric sequences (PCR-derived sequences should be represented by at least two copies during the de-replication step; thus, single copies are probably sequences without biological significance).
The number of de-replicated sequences corresponding to each template is saved in memory by the program to compute an unnormalized abundance. A step of clustering of highly related sequences is applied to correct for PCR amplification and sequencing errors. Because 2% of dissimilarity from any known L1 gene is enough to define a new PV variant  [25] , the tool uses a 98% identity threshold for clustering by default. When searching for new PV types (at least 10% of dissimilarity on the L1 gene), this threshold is a good compromise between sensitivity and specificity, because the potential loss of precision at the variant taxonomic level may be counterbalanced by an increased specificity of the reconstructed sequence. To identify sequences in an unbiased manner, the sequences are aligned against the entire "nt" NCBI database.
Although this step is time-consuming due to the large size of the database, it reduces the false-positive discovery rate. Indeed, querying a database with reduced diversity (such as a virus database) using the e-value as a threshold could increase the chances of getting a hit even if the subject sequence has a low identity with the queried sequence. Considering only the sequences that have their best match against a Papillomaviridae family sequence produces an unbiased result. PVAmpliconFinder includes a grouping step to separate sequences that are putative new PVs from those that are already known PVs, using the threshold of 10% of dissimilarity. This grouping is done before the de novo assembly and classification steps because, although they are partially degenerate, the primers favor the amplification of known PV sequences.
Because the tool is focused on the discovery of new PVs, it is important to separate potential new sequences at the earliest possible stage. A de novo assembly step is performed because of the possibility of using several primer sets that have different hybridization positions along the L1 gene. The objective is to reconstruct the longest possible sequence for each potential PV sequence. PVAmpliconFinder uses an advanced identification and taxonomic classification of the sequences using both sequence similarity and homology. For the sequence similarity, the BlastN algorithm is used against the PaVE database  [5]. This database is the most complete PV database. It includes PV sequences validated by full genome resequencing, but also several "non-referenced" genomes that are not classified taxonomically.
Currently, non-referenced PV genomes in the PaVE database represent more than 37% of the overall available PV genomes (244/649), and this percentage continues to increase  [26, 27]. PVAmpliconFinder presents the results based on the initial MegaBlast step and those obtained based on BlastN alignment against the PaVE database, but a huge number of sequences remain unclassified using the former approach because they match against incomplete L1 cds. Moreover, pairwise alignment with a low percentage of similarity raises a concern about the pertinence of the results obtained. This is especially true for the 3 putative new sequences identified in the application example reported here, because all sequences had at least 15% of dissimilarity against their best match. To circumvent this limitation, we use a complementary approach in parallel based on a molecular evolution method: RaxML-EPA  [28].
In summary, we have developed the first bioinformatics tool for the identification of novel viruses of the Papillomaviridae family from amplicon sequencing data. This tool addresses a gap because no other tool exists for the analysis of this type of data. PVAm-pliconFinder uses an advanced identification and taxonomic classification of the viral sequences extracted, which combines methodologies based on sequence similarity and homology. PVAmpliconFinder produces several tabular and graphical outputs that provide the necessary information to select the most promising putative new PV sequences that may be validated by further wet-lab approaches. Furthermore, PVAmpliconFinder can be easily modified and applied to other viral families, because this would only require a change in the interrogated databases and the reconstruction of a reference tree for the viral family considered.
Image acquisition and processing: Image acquisition for immunodetection was performed using a Zeiss 710 laser confocal microscope (20x and 40x objectives). Quantification of Sox2 and Bra levels in 3D have been made with Fiji or with the spot function (DAPI Staining) of Imaris. Expression have been normalized to DAPI to take into account loss in intensity due to depth, expression and ratios have been calculated and plotted using Matlab. Protein expression after gain and loss of function experiments has been done 7 hours after electroporation by analysing immunodetection signal levels within GFP positive progenitors and normalizing to endogenous expression. Fluorescence distribution in tissues has been acquired on a wide field microscope Axio-imager type 2 (Colibiri 8 multi-diode light source, 10X objective).
The images of electroporated embryos were processed with the Zen software that allows the assembly of the different parts of the mosaic ("Stitch" function). The images were then processed with the "Stack focuser" plugin of the Image J software. The different tissues were delineated on ImageJ with the hands-free selection tool and then the images were binarized using the threshold tool. The total fluorescence intensity emitted for the cells transfected with the different constructs were measured and the sum of the positive pixels for the different tissues was calculated. The percentage distribution of fluorescence in the different tissues was then calculated.
Region of interest were defined manually and their posterior movement was defined by manual tracking of the tailbud movement. Subtraction of the tissue movement was done by defining the average motions of cells in the region. Violin plots were generated on Prism 8 (Graphpad). MSD and distribution of angles were calculated and plotted with a Matlab routine. Angle distribution was calculated from trajectories, weighted with velocities and plotted as rosewind plot using Matlab.
Acknowledgements: We thank  Karine Guevorkian ,  Eric Theveneau , and  Myriam Roussigné  for critically reading the manuscript. We thank  Brice and Stephanie  of the CBI imaging facility and  Marion Aguirrebengoa  for help with statistics. We also thank members of the  Pituello, Soula , Theveneau and Davy teams for suggestions and stimulating discussions during the project.
LSM-BSCF/SDC/LSM-BSCF half-cells (with an RE) were tested inside a ProboStat TM (NorECs Norwegian Electro Ceramics AS, Oslo, Norway) setup system. Two Pt nets were placed on the surface of each electrode as current collectors. Electrochemical impedance spectroscopy (EIS) measurements were carried out through a Solartron Analytical potentiostat (SI 1286) coupled with a frequency response analyzer (SI 1255) and the ZPLOT software (version 14. 1. 3, 45485, Scribner Associates Inc. , Southern Pines, NC, USA) Impedance tests were run between 475 and 650 • C in a frequency range 10 4 -10 -2 Hz. An amplitude varying between 5 and 20 mV was applied, according to the system response. Measurements were performed at open circuit voltage (OCV) conditions as well as under anodic overpotential load, up to +0.
2 V. An N 2 /O 2 = 80/20 v/v% mixture was fed at both sides of the cell, corresponding to a flow of 40 NmL min -1 and 10 NmL min -1 of N 2 and O 2 , respectively. To test the system durability, a 48 h long-lasting test was run under an anodic potential of +0. 150 V, collecting an OCV impedance test every 6 h. Experimental impedance spectra were analyzed by ZView-Impedance Software (version 14. 1. 3, 45485, Scribner Associates Inc. , Southern Pines, NC, USA). To investigate the morphology and the adhesion between the layers, observations were taken by scanning electron microscopy (SEM, Phenom Pro-X) equipped with Energy-dispersive X-ray spectroscopy (EDXS, Bruker) on the as-sintered and tested samples in order to check the distribution of LSM on the top and cross the section of the BSCF electrode.
, 2018). ***Climate data from NOAA (2020): Guba for Guruchai River, Bolnisi for Ktsia River, Gardabani for Mtkvari River and Odzun for Debet River. Data on temperatures are not available. tation was carried out in the laboratory in glass jars, the first four weeks, and then transferred to bottles for transport, with the own yeasts that carried the berries (spontaneous fermentation), for a maximum of 15 days, with a fixed temperature of 20 ºC and daily stirring of the must with the skins of the berries. There was no addition of potassium metabisulfite. The samples were analyzed following the methods proposed by the OIV (2015) in a laboratory accredited under Quality Standard 17025 (ISO 2019), as follows: -Ethanol: Near Infrared (NIR) (SpectraAlyzer WINE, ZEUTEC). -pH and total acidity: Automatic potentiometry (Winelab Analyzer, FOODLAB-CDR, Florence, Italy -Tecnología Difusión Ibérica, Barcelona, Spain).
Sandy beaches are highly dynamic environments buffering shores from storm waves and providing outstanding recreational services. Long-term beach monitoring programs are critical to test and improve shoreline, beach morphodynamics and storm impact models. However, these programs are relatively rare and mostly restricted to microtidal alongshore-uniform beaches. The present 16year dataset contains 326 digital elevation models and their over 1. 635 × 10 6 individual sand level measurements at the high-energy meso-macrotidal rip-channelled Truc Vert beach, southwest France. Monthly to bimonthly topographic surveys, which coverage progressively extended from 300 m to over 2000 m to describe the alongshore-variable changes, are completed by daily topographic surveys acquired during a 5-week field campaign.
Multi-annual/decadal high-resolution (months) topographic datasets of sandy beach systems are, however, scarce. They are limited to microtidal environments  [22] [23] [24]  , alongshore-uniform beaches  [25] [26] [27]  , and/or sectors influenced by coastal structures  [28] [29] [30]  and/or by beach nourishment  30. Monitoring programs of meso-to macro-tidal beaches are based on single of widely spaced transect(s)  17, [31] [32] [33]  , with the exception of the embayed beach of Perranporth, UK  34. Depending on sand grain size and the tide range relative to modal wave conditions  35  , meso-macrotidal surf beaches can exhibit a strongly alongshore non-uniform intertidal and subaerial morphology due to the presence of rip channels, with a typical lengthscale of 100 s of meters  36. Therefore, such beaches need to be surveyed with adequately spaced transects and large enough spatial coverage to comprehensively describe morphological changes.
Truc Vert is a high-energy meso-macrotidal sandy beach located in southwest France (Fig. 1 ). It is a remote beach located more than three kilometers from the first inland carpark beach entry. Apart from the backing coastal dune which has been reprofiled in the early 80 s  37  , Truc Vert has never been nourished or affected by hard structures and tourism. This motivated the first topographic measurements in the late 90 s, surveying irregularly in time a single profile with a theodolite  38. Only in 2003 when a RTK-GNSS receiver was acquired, equipping an ATV since 2005, did the surveys were set monthly and the spatial coverage increased to 300 m. The surveys subsequently became bimonthly, with the alongshore coverage increasing over the years before stabilizing at approximately 2200 m. This dataset was used, for instance, to develop beach state classification  39, 40  , to address the intense beach-dune erosion and recovery from the outstanding winter of 2013/2014  17, 18, [41] [42] [43]  and to address the links between the dominant modes of climate variability on beach response  18.
This monitoring program is also entering a new era as, since 2016, 4 km of coastal dunes are now surveyed at least quarterly  54, 55  , together with notches dug in the dune to reinstate dune mobility and address plant community restoration along with biological monitoring  37. With the growing interest in long-term beach datasets  23, 30  and growing need of validation data for a wealth of coastal models  56  , it is timely to facilitate the unrestricted use of this unique high-energy meso-macrotidal beach dataset, including the daily surveys during the ECORS'08 field campaign. The archived dataset includes all the raw survey points and suitably interpolated digital elevation models (DEMs), and time series of astronomical tide and offshore wave forcing. The beach survey dataset will be updated on a dedicated repository on an annual basis.
We anticipate that this dataset will be of interest for coastal modellers working on shoreline change, rip channel morphodynamics and beach erosion and recovery from the time scale of day to decades. In particular, with the recent development of tens of hybrid shoreline change models, this dataset is an ideal benchmark to test and compare shoreline models and perform ensemble-based simulations  56  and probabilistic shoreline forecast. It will also be used for validation of satellite remote sensing data of coastal change, which is particularly challenging in meso-macrotidal energetic environments  46.
5 m and 3 m with typical amplitude of approximately 50 m. In contrast, shoreline proxy z = 6 m, which approximately corresponds to the dune toe position barely evolves and only retreated rapidly during the outstanding winter of 2013/2014. ECORS'08 experiment. The ECORS'08 experiment was a 5-week large multi-institutional nearshore field experiment conducted at Truc Vert from March 3 to April 8, 2008  52. During this experiment, almost daily topographic surveys were collected  71. In addition to the ATV survey areas, a walking operator also sometimes collected topographic data in moderate water depths. Here we analysed the topographic dataset and the couple of topographic surveys performed just before this experiment using the same method as detailed above. The alongshore coverage of the surveys was irregular but was at least 1000 m (Fig.
astronomical tides. Astronomical tides were derived at 10-min intervals using the tidal analysis package T Tide. A coastal hydrodynamic hindcast of the Bay of Biscay 75 was used. Tide hindcast offshore of Truc Vert in approximately 7-m depth at spring low tide from January 2006 to January 2020 was used to perform the harmonic analysis. The tide time series was validated against 20 days of interspersed tide measurements offshore of Truc Vert during the ECORS'08 measurements.
Data records The data can be obtained from the Open Science Framework Repository  76. All files are in NetCDF format  77  , and provide detailed metadata for each variable within the file, using CF (Climate and Forecast) conventions 1. 8 with Standard Name Table  v75. The core data set in the repository is formed by 326 DEMs, all interpolated on the same regular grid, along with their raw sand level measurements from September 10, 2003  daily during the ECORS'08 experiment. The metadata of the topographic surveys are given in Table  1. This dataset is completed with hourly time series of wave conditions co-located with the offshore CANDHIS buoy in 54-m depth, and astronomical tide levels at 10-min interval (Table  2 ). Given that some shoreline change models need wave conditions up to 3 years prior to the start of shoreline measurements to accurately compute equilibrium  conditions  2, 21, 50  , wave and tide time series start on January 1, 2000, that is, more than three years before the first topographic survey.
Technical Validation Topographic surveys. All sand level measurements were performed using a RTK-GNSS (Trimble 5700 then Trimble NetR9 for the reference receiver, and Trimble R6 then Trimble R8s for the mobile GNSS receiver) equipping the ATV (Fig. 1b ). The horizontal and vertical accuracy is 8 mm and 15 mm, respectively. However, given that additional small errors can be due to e. g. antenna positioning, presence of ripples and megaripples, an accuracy of approximately 2. 5 cm in the horizontal and 10 cm in the vertical is conservative. All surveys recorded the position of a fixed reference point on the top of dune, which was used for vertical and horizontal accuracy verification on each survey. All surveys were quality checked. Rare survey points outliers were manually removed. Eight topographic surveys were removed from the current dataset due to RTK-GNSS malfunctions and varying, irredeemable, elevation drift in time.
We used in situ wave data collected intermittently between January 2008 and October 2019 resulting in a total of approximately 8. 5 years of hourly wave data (n = 73,672). Peak wave period, significant wave height and mean wave direction were linearly regressed against measurements. After correction, H s hindcast shows strong agreement with measured wave data with for HOMERE (NORGAS-UG) a coefficient of determination R 2 = 0. 94 (0. 93) and a root mean square error RMSE = 0. 25 m (0. 26 m). Accuracy decreases for T p (R 2 = 0. 25, RMSE = 1. 7 s for HOMERE and R 2 = 0. 37, RMSE = 1. 6 s for NORGAS-UG). The decrease in accuracy for T p can be partly explained by discontinuities in measured and modelled peak wave period data, which can vary substantially under mixed sea/swell regimes and when there is bimodality in the wave spectra.