dataesr/scientific-paragraphs-categorization huggingface dataset:
SeaVision measurements were validated against co-located Spotter wave buoy data and intercompared with the output of WaveWatch III simulations. Observations of the wind waves with the navigation X-band radar were found to be in good agreement with buoy data and model simulations with the best match for the wave propagation directions. Supporting datasets consist of significant wave heights, wave directions, wave periods and wave energy frequency spectra derived from both SeaVision and the Spotter buoy. All supporting data are available through the PANGAEA repository -https://doi. org/10. 1594/PANGAEA. 939620 (Gavrikov et al. , 2021). The dataset can be further used for validation of satellite missions and regional wave model experiments. Our study shows the potential of ship navigation X-band radars (when assembled with SeaVision or similar systems) for the development of a new near-global observational network providing a much larger number of wind wave observations compared to e.
Such a design allows for the maintenance of at least 300 m distance between the buoys and the R/V. Then, both buoys were in the free-floating mode for at least 30 min, during which the recording was performed by both SeaVision and the Spotter buoy (Fig. 4a ). Lastly, both buoys were pulled back on board. The Spotter buoy measured vertical and horizontal displacements, starting from its release until being retrieved back on board. After completing measurements at each station, only the data recorded during the freefloating mode were used for the joint analysis of SeaVision and Spotter buoy records. During all SeaVision and Spotter buoy measurements, standard meteorological parameters were measured using the onboard meteo station. Development of the SeaVision system was based on a commonly accepted approach to the recording and analysis of the sea clutter images.
Spotter wave buoy data To calibrate and validate SeaVision wave observations, we performed simultaneous measurements with the Spotter   wave buoy (https://www. sofarocean. com/products/spotter, last access: 4 August 2022) in the locations shown in Fig. 1  and specified in Table  A1. Once the ship was drifting at the locations of the measurements, the Spotter buoy was deployed and started drifting away from the ship. Note that the ship drift is always faster compared to that of the buoy; thus, the distance between the buoy and the ship progressively increases. When the distance between the ship and the buoy reached at least 300 m, the "free-floating" mode of SeaVision and Spotter buoy operation was initiated for at least 30 min as described in Sect. 2. 1. The longest free-floating-mode time period at some stations reached up to 1.
For low wind speeds SeaVision tends to underestimate H s by up to 60 cm, and for moderate and strong winds the analysis shows an overestimation of SeaVision H s compared to buoy and model data. This can be explained by better-developed   ripples (affecting the signal-to-noise ratio) at the ocean surface under stronger winds. We also identified three locations (2901, 2928, and 2937; see Table  A1 ) for which the differences between the Spotter buoy data and SeaVision reach more than 1 m (for 5 and 13 m s -1 winds). Weather conditions for these cases were not associated with severe weather and H s values were in the range between 1. 5 and 2 m. However, in these cases we recorded a strong drift of the vessel due to the local current that potentially impacted the angle of the electromagnetic signal reflection from the surface and hence affected the accuracy of the radar images.
Data availability Datasets that contain significant wave heights, wave periods, wave directions, wave energy frequency spectra, meteorological data, and other related parameters from both SeaVision and the Spotter buoy at the locations of every station (Table  A1 ) are available in the PANGAEA repositoryhttps://doi. org/10. 1594/PANGAEA. 939620 (Gavrikov et al. , 2021). In this dataset we provide wind wave statistics disregarding separation of the swell and wind waves at this stage of the SeaVision development. We plan to include this proce- dure in the next studies. At the same time, we provide a 1D spectrum that potentially allows us to see the first and second peaks associated with wind waves and swell (an example is shown in Fig. 4b ). Users interested in the analysis of the raw radar dataset or in the wave characteristics in the locations where measurements were carried out only with SeaVision are welcome to request access from Alexander Gavrikov (gavr@sail.
The authors thank the Floating University scientific and educational programme and Natalia Stepanova, Alexander Osadchiev, Sergey Gladyshev, and Vsevolod Gladyshev of the Shirshov Institute of Oceanology for their leading roles in organization of research cruises. We are also thankful to Vika Grigorieva and Igor Goncharenko of the Shirshov Institute of Oceanology for their help with setting up the hardware for radar signal digitization and useful advice. We thank Takaya Uchida at the Université Grenoble Alpes for final proofreading of the manuscript. Financial support. This study was funded by the Russian Foundation for Basic Research, project no. 20-35-70025. VS and AG were also supported with grant no. 17-77-20112-P from the Russian Science Foundation (WW3 setting for the period of the research cruises).
Here we show that cooperation between SL1 and the longer UBTF1 splice variant generates the specificity required for rDNA promoter recognition in cell. We find that conditional deletion of the Taf1b subunit of SL1 causes a striking depletion UBTF at both rDNA promoters but not elsewhere across the rDNA. We also find that while both UBTF1 and -2 variants bind throughout the rDNA NFR, only UBTF1 is present with SL1 at the promoters. The data strongly suggest an induced-fit model of RPI promoter recognition in which UBTF1 plays an architectural role. Interestingly, a recurrent UBTF-E210K mutation and the cause of a pediatric neurodegeneration syndrome provides indirect support for this model. E210K knock-in cells show enhanced levels of the UBTF1 splice variant and a concomitant increase in active rDNA copies.
Genetic ablation of the SL1 subunit Taf1B has now revealed an unexpected sequence specific role for Ubtf and has suggested a novel induced-fit model for RPI preinitiation complex formation. Though it was assumed from cell-free studies that SL1 would be essential for cell and organism survival due to its role in rDNA transcription, this had not been directly tested. Our data show that homozygous deletion of the gene for the SL1 subunit Taf1b prevented mouse development beyond blastocyst while heterozygous deletants were viable and fertile. Hence, as observed for the other RPI basal factors Ubtf and Rrn3, Taf1b is an essential factor in mouse. Conditional deletion of taf1b in MEF and mES cell culture was also found to arrest rDNA transcription and to cause severe disruption of nucleolar structure characteristic of nucleolar stress  (24, 28).
Depletion of Taf1b also prevented promoter recruitment of Taf1c and TBP subunits of SL1 and hence PIC formation at both the 47S pre-rRNA and the Enhancer-associated Spacer rDNA promoters. Quite unexpectedly, this also led to a loss of Ubtf at both these promoters, though not elsewhere across the rDNA NFR. ChIP-qPCR and high resolution DChIP-Seq showed that the loss of Ubtf from the promoters was proportional to the loss of SL1, strongly arguing that binding of these two basal factors was cooperative. Conversely, we had previously shown that in cell loss of Ubtf eliminated SL1 from the rDNA promoters  (24, 26) , consistent with the cooperative recruitment of these factors. Data from early cell-free studies had suggested two possible scenarios for RPI preinitiation complex formation, either SL1 recruitment depended on prebinding of Ubtf or conversely that Ubtf recruitment depended on pre-binding of SL1  (22, 30).
Our data resolve this contradiction by showing that in cells Ubtf and SL1 binding at the rDNA promoters is strongly interdependent, neither factor being recruited in the absence of the other. The lack of Ubtf binding at the promoters in the absence of SL1 was particularly surprising, especially so since Ubtf remained bound throughout the rest of the rDNA NFR and even at immediately promoter adjacent sites. Thus, the absence of SL1 the RPI promoters rather than being prefer sites of Ubtf binding as usually assumed, are quite on the contrary sites of low Ubtf affinity lying within the NFR continuum of higher affinity sites. Our data further revealed the key importance of the Ubtf1 variant in the recruitment of SL1 to the rDNA promoters. Mouse and human cells express varying levels of the Ubtf1 and Ubtf2 splice variants that differ by a 37a.
a deletion in HMGbox2 of Ubtf2 (Figure  4 ). By mapping these variants across the rDNA we found that Ubtf1 was recruited to the rDNA promoters at least four times more often than Ubtf2, though the data were also consistent with the exclusive recruitment of UBTF1 at the promoters. In contrast, Ubtf1 and Ubtf2 bound indistinguishably elsewhere across the rDNA. Since only Ubtf1 is present in mESCs, deletion of taf1b in these cells also clearly demonstrated that promoter recruitment of Ubtf1 depended on SL1 (Figures  S4  and S5 ). Thus, formation of the RPI preinitiation complex is driven predominantly if not exclusively by a cooperation between SL1 and Ubtf1. This provides the first mechanistic explanation for why Ubtf1 is absolutely required for rDNA activity in vivo  (31). Recruitment of Ubtf1 and SL1 was found to be cooperative not only at the major 47S rDNA promoter but also at the enhancer associated Spacer promoter.
Since these promoters display little DNA sequence homology, this raised the question of what in fact defines an RPI promoter and how is it recognized? Our data clearly show that promoter recognition involves the cooperative recruitment of Ubtf1 and SL1. Previous data showed that Ubtf interacts with SL1 solely via its highly acidic Cterminal tail, an ~80 a. a. domain containing 65% Asp/Glu residues  (32). However, this domain is not essential for cell-free transcription  (33)  and is anyhow present in both Ubtf variants. So, while it might play some role in bringing SL1 to the promoters it cannot explain their selective binding of Ubtf1. Coimmunoprecipitation also failed to detect any specific interaction between SL1 and one or other of the Ubtf variants (data not shown). Thus, it seems unlikely that the rDNA promoters are recognized by a pre-formed SL1-Ubtf1 pre-initiation complex.
Isolation and culturing of Taf1b conditional MEF and mES cells. Conditional Taf1b primary mouse embryonic fibroblasts (MEFs) were generated from E14. 5 taf1b fl/fl /ER-Cre +/+ /p53 -/-and wild type control ER-Cre +/+ /p53 -/-embryos as previously described  (26, 36) , and were genotyped by PCR as described for mice, see Supplementary Data (Figure  S1A  and B ). Mouse Embryonic Stem (mES) cells were derived from the inner cell mass of Taf1b fl/fl /ER-Cre +/+ and wild type control ER-Cre +/+ blastocysts essentially as published  (37). After establishment of the cells on feeder monolayers, they were adapted to feeder-independence on 2i/LIF N2B27 (ThermoFisher) free serum medium  (38)  and subsequently maintained in this medium. The taf1b fl/fl /ER-Cre +/+ and control mESCs were genotyped as for MEFs.
ChIP-qPCR analysis. All ChIP experiments included a minimum of 2 biological replicates and were analyzed as previously described  (24). For qPCR analysis, reactions (20 μl) were performed in triplicate using 2. 5 μl of sample DNA, 20 pmol of each primer, and 10 μl of Quantifast SYBR Green PCR Master Mix (QIAGEN) or PowerUp™ SYBR™ Green Master Mix (ThermoFisher). Forty reaction cycles of 10 s at 95 C and 30 s at 58 C were carried out on a Multiplex 3005 Plus (Stratagene/Agilent). The amplicon coordinates relative to the 47S rRNA initiation site (BK000964v3) were as follows: IGS3, 42653-42909; SpPr, 43076-43279; Tsp, 43253-43447; T0/Pr (47SPr), 45133-40; 47S-5', 159-320; ETS, 3078-3221; 28S, 10215-10411; T1-3, 13417-13607. Data was analyzed using the MxPro software (Agilent). The relative occupancy of each factor was determined by comparison with a standard curve of amplification efficiency for each amplicon using a range of input DNA amounts generated in parallel with each qPCR run.
ChIP-Seq and data analysis. ChIP DNA samples were quality controlled by qPCR before being sent for library preparation and 50 base single-end sequencing on an Illumina HiSeq 2500 or 4000 (McGill University and Genome Quebec Innovation Centre). Sequence alignment and deconvolution of factor binding profiles to remove sequencing biases (Deconvolution ChIP-Seq, DChIP-Seq) were carried out as previously described  (9, 24). The manual for the deconvolution protocol and a corresponding Python script can be found at https://github. com/mariFelix/deconvoNorm. Gaussian curve fitting to transcription factor binding profiles was perform using MagicPlot Pro (Magicplot Systems) on data from the DChIP-Seq BedGraph files. The raw sequence files and the processed deconvolution BedGraphs have been submitted to ArrayExpress under accession E-MTAB-10433.
Cell Proliferation Assay. Cells from two ubf wt/wt MEF clones (#3, #4) and three ubf E210K/E210K MEF clones (#1, #2, #3) were continuously cultured for more than a week prior to assay. Cells were plated at ~500 per well in 96-well plates and cultured for six days. At each timepoint, duplicate wells were treated with Hoechst 3342 (Invitrogen, Thermo Fisher Scientific) for 45min. Images were acquired using Cytation5 (Cell Imaging Multi-Mode Reader by BioTek) and cell counts for each clone were determined using the Gen5 software.
Total RNA Extraction and Quantification Cells were trypsinized, counted and total RNA was recovered from 3x10 6 cells using 1 ml of Trizol (Invitrogen, Thermo Fisher Scientific) according to the manufacturer's protocol. RNA yields were determined using Qubit RNA BR (Invitrogen, Thermo Fisher Scientific). showing detail over the promoter and enhancer regions. D) Peak fit analysis of Ubtf1 and Ubtf2 occupancies over the Spacer and 47S promoter revealed that Ubtf1 was at least 4 times more prevalent at either promoter. The data derive from two biological replicas and the SEM is shown. FIGURE LEGENDS Figure  5. The E210K Ubtf mutation suppresses both proliferation and 47S pre-rRNA synthesis but enhances rDNA activation. A) ubtf E210K/E2109K MEFs were found to proliferate significantly more slowly than isogenic wild type (Ubtf wt/wt ) MEFs.
Doubling time (dt) was estimated from an exponential curve fit as respectively 35h and 31h for mutant and wild type MEFs. B) 47S pre-rRNA synthesis in Ubtf E210K/E2109K and wild type MEFs determined by metabolic pulse (30 min) labelling. See also Figure  S9A  for analysis of processing intermediates at increasing labelling times for individual MEF isolates. C) Per cell total cellular RNA content of Ubtf E210K/E2109K and wildtype MEFs. The data in A, B and C derive from two or more biological replicas in each of which a minimum of two independently isolated mutant and wild type MEF cultures were analyzed in parallel. D) PAC analysis of Ubtf E210K/E2109K and wild type MEFs. Upper panel shows an example of the active rDNA "a" and inactive "i" profiles for the 1. 3kbp BamHI-BamHI 47S coding region fragment (see Figure  2A ) and the lower panel corresponding band intensities.
profiles. E) Active rDNA fractions were estimated from the combined curve fit analysis of 1. 3, 2. 4 and 4. 7kbp BamHI-BamHI rDNA fragment PAC profiles. The data derive from three independent Ubtf E210K/E2109K and two wild type MEF isolates in two PAC biological replicas and are plotted to show median, upper and lower data quartiles and outliers. F) and G) Analysis of Ubtf1 and 2 levels in Ubtf E210K/E2109K and wild type MEF isolates. Panel F shows a typical Western analysis of Ubtf variants in these MEFs and panel G quantitative estimates of relative Ubtf1/Ubtf2 protein and mRNA ratios in these MEFs. H) and J) Show similar estimates of relative Ubtf1/Ubtf2 protein and mRNA ratios in Cortex and Cerebellum tissue from matched Ubtf wt/wt , Ubtf wt/E2109K and Ubtf E210K/E2109K adult mice. Error bars throughout indicate SDM.
ACKNOWLEDGEMENTS We would like to thank the staff of the  "Centre d'Expertise et de Services Génome Québec"  for Next-Generation Sequencing,  Marianne Sabourin-Felix  ( Cancer Division, CRCHU de Québec-Université Laval ) for sequence alignment and DChIP-Seq deconvolution and  Mark Robinson  and  Helen Lindsay  ( IMLS/SIB, University of Zürich ) for making their computing facilities and advice available to us.
To this end, the dataset encompasses (1) a variety of protein domains, with some of them closely related, to query the dataset, (2) a dataset of experimental structures that contain one or more domains, (3) a few protein shapes corresponding to protein that contains two of the query domains, (4) two versions of the same dataset, one made of the protein shapes only, and the other with an additional physico-chemical property, the electrostatic potential, encoded along the shapes. We selected this surficial physico-chemical feature as it is the main driving force in many biological recognition processes, such protein-ligand and protein-protein interactions  [14]. In the present work, we detail the dataset proposed by the challenge organizers to the participants and how it differs from the previously proposed datasets in Section 2.
Constitution of the SHREC ′ 21 dataset The SHREC ′ 21 protein dataset is based on the Pfam 33. 1 database  [16]. Basically, this database classifies protein sequences into domains and families, that can be grouped into clans whenever they are evolutionarily related. Protein domains of structures from the Protein Data Bank (PDB  [17, 18] ) can therefore be attributed to a Pfam domain and, possibly, a clan. To build up the track dataset, we relied on this notion of domain, and manually selected 10 Pfam domains: the SH2 domain (PfamID PF00017), the SH3 domain (PfamID PF00018), the variant SH3 domain (SH3_2, PfamID PF07653), the PDZ domain (PfamID PF00595), the PDZ_6 domain (PfamID PF17820), the peptidase family M50 (m50, PF02163), the bromodomain family (PF00439), the DNA-binding domain of the STAT protein (STAT-binding, PF02864), the PHD-finger domain (PfamID PF000628), and the C2H2 Zinc-finger domain (zf-C2H2, PfamID PF00096).
For each selected domain, all corresponding structures from the PDB were listed, and the best resolution structures were retrieved to serve as a query for the track. When applicable, the NMR (Nuclear Magnetic Resonance) structures were assigned an arbitrary resolution of 2. 25 Å  [19] , while structures with no resolution were discarded. The residues corresponding to the Pfam domains were then extracted from the selected structures when necessary, so that the selected domains were left alone. For example, only the DNA-binding domain of the STAT (Signal Transducer and Activator of Transcription) protein was kept as a query, its others domains being discarded. The remaining structures were filtered according to their Uniprot  [20]  identifier, and duplicates were discarded to (1) ensure a diversity of sequence structures among the dataset, (2) limit the dataset size to a tractable size given the track timeline.
Finally, only the best resolution structures for each Uniprot entry were kept (Table  A. 6). When NMR structures were selected, only the first model was considered. Unlike the query structures, we kept the other domains present in these structures that eventually constitute the dataset. Therefore, many dataset structures display several domains, at least one of which is one of the query domains. For all structures (queries and dataset structures), we removed all hetero-atoms, and unwanted chains. The resulting PDB structures were then protonated using pdb2pqr  [21] , using propka  [22, 23]  to compute the pKa values of the ionizable groups at pH = 7. 2. The solvent-excluded surface of all protonated structures were computed using the default parameters of EDTSurf  [24, 25]  except that inner cavities were discarded.
We then computed the electrostatics using APBS suite  [26] , and used the multivalue program to compute the electrostatic potential at the mesh vertices locations. Two datasets were then assembled, one with only the protein surface shapes (shape-only dataset) and one combining the protein surface shapes and electrostatics values (shape + electrostatic dataset). Similarly, two sets of query surfaces were produced (shape-only and shape + electrostatic). Each dataset (shape-only and shape + electrostatic) includes 554 molecular surfaces which were made available to the track participants, along with the 2 sets of 10 queries, on the track web-page (http://shrec2021. drugdesign. fr). Regarding the dataset, it is important to note that SH3 and SH3_2 domains were annotated as similar according to HHsearch (a tool commonly used to detect homologous proteins  [27] ), as well as the PDZ, PDZ_6 and m50 domains.
We present in Fig. 1  the TM-scores matrix for all queries of the dataset. A TM-score above 0. 5 indicates that the two structures are likely to share the same topology, while unrelated structures are usually associated to TM-scores below 0. 17  [28]. SH3 and SH3_2 query structures show a TM-score of 0. 84, while, PDZ and PDZ_6 query structures show a TM-score of 0. 79. Surprisingly, the m50 query structure only has a TM-score of 0. 28 and 0. 32 with PDZ and PDZ_6 structures, respectively. A visual inspection of these structures confirmed that the peptidase M50 is topologically different from both the PDZ and PDZ_6 domains: while the peptidase M50 is mainly α-helical, PDZ and PDZ_6 are mixed αβ proteins. Overall, most query structures present an intermediate topological similarity with all other queries, as evidenced by the fact that all TM-scores range from 0.
19 to 0. 47, except for the aforementioned pairs of classes (SH3/SH3_2 and PDZ/PDZ_6). Finally, as the dataset encompasses multi-domain proteins, 22 dataset proteins display two of the query domains. Namely, 9 proteins of the dataset encompass both a SH2 and a SH3 domains, 6 proteins encompass both a SH2 and a Stat-binding domains, 4 proteins encompass both a bromodomain and a PHD-finger domain, 2 proteins encompass a PDZ_6 and a peptidase family M50 domains, and one structure encompass a PDZ and a peptidase m50 domains. The final structure of the dataset and the size of each class is summarized in Fig. 2.
Comparison to previous SHREC datasets on proteins Compared to previous SHREC datasets dealing with protein structures or surfaces  [29] [30] [31] [32] [33] [34] , the SHREC ′ 21 protein track dataset is characterized by three main aspects: (1) the presence of two datasets representing the same set of proteins, one shape-only and one shape + electrostatic dataset, (2) the close evolutionary relationship of some of the query domains, further characterized by a similar topology (Fig. 1 ), (3) the intermediate similarity of the domains topologies (Fig. 1 ), and (4) the use of individual domains to query a dataset of single-as well as multi-domain proteins shapes. The main novelty of the SHREC ′ 21 track is arguably the availability of protein surfaces with electrostatic values, which has been shown to improve the retrieval performance of protein surfaces  [11, 35].