dataesr/scientific-paragraphs-categorization huggingface dataset:
For cellular organisms (such as Bacteria, Archaea, and microbial eukaryotes), a vast expansion in the number of available genome sequences and updated analytics enabled a systematic genome-based classification that has had a profound impact on taxonomy  [7 ,8,9]. Techniques for cultivation-independent discovery of viruses, including metagenomic analysis and single-particle sequencing, as well as database mining efforts have contributed sequence data for hundreds of thousands of previously unknown viruses  [2, [10] [11] [12] [13] [14] , with advances in overall virus taxonomy following closely behind  [15,16 ,17]. As genome sequences of uncultivated viruses provide new detailed information about the complex virosphere, the International Committee on Taxonomy of Viruses (ICTV) has the challenge of robustly classifying this unprecedented diversity.
Other tools exploit this information to distinguish viral and cellular sequences in whole-community datasets, picking out the viral needles from the metagenomic haystack  [23]. Different viral strains or subtypes can be distinguished through viromics, opening up new possibilities to distinguish evolutionary and ecological dynamics of uncultivated viruses in infected hosts and in natural biomes. Recent benchmarking studies based on simulated or mock community data provide information on the advantages and disadvantages of different computational tools for identifying and classifying viruses  [24] [25] [26]. Viruses that are relatively closely related to known ones can be identified by direct sequence-similarity searches of whole genomes or taxon-specific hallmark genes, which are also used for meaningful phylogenies and taxonomic classification.
Viruses that are relatively unknown, representing new members within higher ranks require sensitive profile hidden Markov model-based sequence-similarity searches and assessment of the statistical significance of the hits  [26]. Moreover, fundamentally different approaches have been used, including using the absence of known gene families as a signal for identifying viral sequences  [27]  and homology-independent features, such as genomic coding structure including directionality of genes, intergenic regions, or replichores  [28, 29] , and nucleotide usage patterns  [30]. These genomic features may be extracted computationally from viral sequences and encoded into machine-learning tools to identify viruses in metagenomic data. To facilitate taxonomic efforts, newly discovered viral sequences need to be consistently deposited into databases.
Recent breakthroughs in high-throughput discovery of uncultivated viruses notwithstanding, there is a bottleneck in annotation and taxonomic classification. The ICTV ratifies hundreds of taxonomy proposals each year  [18] [19] [20] , but the rate of virus discovery is several orders of magnitude higher. As a result, the number of viruses that are represented and classified in the International Nucleotide Sequence Database Collaboration (INSDC) and RefSeq databases, which implement ICTV's taxonomy, remains relatively limited. Databases that gather the sequences of uncultivated viruses, such as the Integrated Microbial Genome/Viral Resources (IMG/VR) database, are more inclusive but necessarily also less curated, lacking manual annotation and potentially containing occasional 'false-positives' (i.
e. , sequences from non-viral organisms), which are an inevitable result of using computational tools to make sense of the data  [32, 33]. It is also important to note that the performance of virus-identification tools is often assessed in cross-validation tests, assembled by randomly extracting training and testing data from the available sequences in the database (e. g. , in an 80:20 ratio, respectively). Such practices should be carefully designed to account for the biased composition of databases. The sequences extracted from a database do not typically have the same degree of novelty and diversity as a real dataset, and this biased representation may result in overestimation of a tool's performance. A promising approach is to omit sequences from entire higherrank taxa, such as viral families from the training data to mimic their novel discovery, as was done for bacterial taxa to benchmark the taxonomic classification tool CAT  [34].
Taxonomy proposals are processed as part of a yearly cycle, with a deadline around May or June for submission to the relevant ICTV Subcommittee Chair, ICTV Executive Committee approvals over the summer, proposal revisions, voting by ICTV Members in October or November, and ratification around February. After ratification, the new taxonomy may be incorporated into public databases. For further details and contact information, please see the ICTV website at https://ictv. global.
Assembling and validating uncultivated virus genome sequences Viral sequence assembly is performed with specialized software that generates contiguous sequences from shortsequencing reads, such as, Metaviral SPAdes (which exploits the specific coverage profile of viral contigs in metagenomes)  [36]  or SAVAGE (which can differentiate viral strains with sufficient coverage)  [37]. Especially in metagenomes, sequences might be mis-assembled, so it is important to assess sequence validity. Benchmarking studies have shown that chimeras and mis-assemblies are rare but depend on the assembly program and parameter settings  [38, 39 ]. Because chimeric assemblies may occur more frequently among less-abundant genomes with incomplete horizontal coverage, high numbers of chimeras have been observed among shorter contigs, also a consequence of low abundance  [40].
Moreover, chimeras are more likely to occur among genomes of organisms that share high levels of sequence similarity, such as closely related viral populations. Another issue that might occur is artificial replication of regions or the entire contig in the assembly. Assembled sequences should be investigated for potential assembly errors-for example, by mapping the sequencing reads to the assembled contigs, observing the depth-of-coverage profile, mapping of mate-paired reads, and multi-mapping reads. Long-read sequencing technologies may be used to validate contigs assembled from short-sequencing reads or bypass shortread sequencing altogether  [41]. Recovering the same or highly similar sequences multiple times independently from different datasets or samples provides strong evidence for their validity.
For example, two almost-identical sequences (one polymorphism in 96 908 nucleotides) of crAss-like bacteriophages were recovered from two gorilla feces samples  [42]. Validity investigations and all experimental and computational methods used to identify uncultivated virus sequences should be clearly described in the taxonomy proposal. Moreover, it is critical that the raw data are deposited in one of the INSDC sequence-read archives so researchers can reexamine the original data, should any doubts arise. Estimating the completeness of uncultivated viral genome sequences is a challenge that deserves special attention, since new taxa should only be proposed if at least coding-complete genome sequences of representative viruses have been determined. Arguably the strongest indication of a genome sequence being complete is its similarity in gene content to genomes from known viruses or to sequences that were independently assembled.
The availability of many closely related virus genome sequences enables the structure of the taxonomy to be resolved in much more detail than is possible with only a few viruses. For example, fine-grained typing is possible in highly sampled clades of human-infecting viruses  [60] , whereas gene-sharing networks are revealing a coarse-grained structure of the taxonomy of bacteriophages  [6]. A minimal requirement for valid automation of virus taxonomic assignment is the availability of the genome sequences of all previously identified viruses that are part of the taxonomy. Indeed, sequence similarity is one of the strongest signals for identifying viral sequences, estimating genome completeness, identifying genes, and predicting gene functions. Thus, expanding the virus sequence database is essential to make sense of the global virosphere  [61, 62].
Exemplar virus genome sequences of all ICTV-ratified virus species are available in INSDC databases, a requirement for their recognition by the ICTV and a guarantee that they can be sustainably accessed. The accession numbers for these sequences are available through the virus metadata resource (VMR, see https://ictv. global/taxonomy/vmr/). However, many more virus sequences belonging to these species are present in databases, and it may be difficult to identify them without performing specific searches and in-depth sequence analyses. Ultimately, the inclusion and demarcation criteria for all taxa should be made available in a machine-readable format so that they may be programmatically accessed and readily applied to viral sequences to support their classification.
Acknowledgements The authors thank  Anya Crane  ( National Institutes of Health [NIH] National Institute of Allergy and Infectious Diseases [NIAID] ) for critically editing the manuscript. The authors are members of the  ICTV Virus Bioinformatics Expert Group. BED is supported by the  European Research Council (ERC)  Consolidator grant 865694: DiversiPHI. CL is supported by the  Research Foundation Flanders (FWO) SB  grant  1S64720N. RAE is supported by the  National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (NIH)  under Award Number  RC2DK116713. SR is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. This work was also supported in part through  Laulima Government Solutions , LLC, prime contract with the  NIH National Institute of Allergy and Infectious Diseases (NIAID)  under Contract No.
DATA AND METHODS This outcrop-based study integrates traditional and digital field data acquired during three field campaigns  (2018, 2019, 2020)  in the southern portion of the Hikurangi subduction wedge (Coastal Ranges, eastern North Island of New Zealand), and focuses on the shelf-derived gravity-driven deposits of the Waikaraka and Homewood localities (Figure  3 ; wk and hw sections). Fieldwork consisted of logging sedimentological profiles (total of 312 meters at 1:50 bed-scale), measuring structural (e. g. , ductile deformation analysis) and stratigraphic features (e. g. , bedding, paleocurrent) as well as describing each shelf-derived flow deposit in a standardized manner (facies and geometries). Owing to the abundant macrofossils contained in the deposits, occurring both as whole skeletons and fragmented remains, five taphonomic analyses were performed on approximately one square meter areas for both the outcrops (i.
e. , four at Waikaraka and one at Homewood). These analyses were restricted to the coarsest fraction (larger than five millimeters) of death assemblages contained in the deposits. Three categories of skeleton damages, namely fragmentation, abrasion and bioerosion, were described visually using the graded classification scale presented in  Appendix 1, complemented by Caron (2011)  and  Caron et al. (2019). Seven samples were collected to supplement the Fossil Record Electronic Database (https://fred. org. nz/) since both areas are currently mapped as Quaternary beach deposits on the 1:250 000 Geological Map of New Zealand (QMAP) of Wairarapa (https://www. gns. cri. nz/). The micro-and macropaleontological analyses were conducted by GNS New Zealand and allowed to determine the age of the sedimentary units and related depositional paleobathymetries (Appendix 2; Appendix 3).
ACKNOWLEDGEMENTS This research was funded by  Schlumberger. We are grateful to  Karen  and  John Barbour  for their warm welcome during our stays at  Homewood  and for access to the studied areas. Special thanks to  Pierre Malié  and  Andréa Barrier  for their support during the fieldwork missions, as well as to  Romain Armand ,  François-Xavier Joanny  and  Pierre Saulet from UniLaSalle (France)  for their technical assistance on the GIS and outcrop modelling workflows. We also thank our colleagues from the  Basins-Reservoirs-Resources B2R research unit (University of UniLaSalle, France ) for their lasting support and  Hugh Morgans  and  Henry Gard  from  GNS Science  ( Lower Hutt, New Zealand ) for the paleontological analyses. Finally, we thank the Editor  Roberto Tinterri  and the reviewers,  Bradford Prather  and  Domenico Chiarella  for their helpful and constructive comments and suggestions.
Unique sequences in TRF1 Sequence alignment of TRF1 revealed unique amino acid changes within two highly conserved sequence blocks (Fig. 1A ), including Ala75Thr (position based on Protein Data Bank no. P54274), which was found to be positively selected in the NMR  (9)  and located in the homodimerization domain, and Ala105Thr, which was implicated in TIN2 binding. Ala75Thr is located in helix 1 within a hydrophobic region involved in the interaction with helix 9, which shows no compensatory mutations. Replacement of alanine with threonine may slightly bend the helix containing this site and affect hydrophobicity of this area, influencing TRFH homodimerization. Ala105Thr is located in the center of a deep hydrophobic pocket of the TRFH domain in the TIN2 binding area, suggesting that it may affect TRFH homodimerization and TIN2 binding (Fig.
The 5′UTR tree correctly clustered the two paralogous gene groups, resulting in a tree topology consistent with a single Tinf2 duplication predating the split of Hystricognathi (fig. S5 , C to E). Complemented with manual sequence alignment inspection, we concluded that gene conversion occurred between Tinf2 paralogs in the genomic region that comprises their entire coding sequences and introns, but not their full 5′UTRs. Next, we assessed the effect of the Tinf2 duplication on gene expression using an RNA-seq dataset that included diverse mammalian species and spanned multiple tissues  (49). We quantified the RNA-seq read mapping to the coding sequences of Tinf2 genes. Because of the high similarity of NMR Tinf2 paralogs, reads coming from either gene cannot be distinguished so that the resulting quantification represents their aggregated expression level.
RNA-seq and analysis RNA quality was checked using Bioanalyzer 2100 (Agilent Technologies). Indexed cDNA libraries were obtained using the Ovation Solo RNA-Seq System (NuGen) following the manufacturer's recommendations. Multiplexed libraries were loaded on a NovaSeq 6000 system (Illumina) using an S2 flow cell, and sequences were produced using a 200-cycle kit. Paired-end reads were mapped against the mouse reference genome GRCm38 using STAR software (version 2. 5. 3a) to generate read alignments for each sample. Genelevel counts were obtained using HTSeq  (50). Differential gene expression analysis was performed using DESeq2 R package  (51). P values were corrected for multiple testing using the Benjamini and Hochberg default method. The adjusted P values were filtered with a cutoff of 0.
Acknowledgments: We thank  R. Liao  for the use of microscopy and flow cytometry facilities and  W. Oldham  and  J. Loscalzo  for the use of a hypoxia chamber and the  Seahorse facility. Sequencing was performed at the  Brussels Interuniversity Genomics High Throughput core  (www. brightcore. be). We also thank  F. Miot ,  X. De Deken , and  I. Pirson  for some aliquot of antibodies against tag V5 and Flag (M2). Plasmid TPP1-V5 was provided by  Z. Songyang  and  D. Liu. We also thank  A. Leon Trapp  who coordinated transfer of materials. Funding: The V. N. G. laboratory is supported by  NIH  grants, and the E. G. laboratory is supported by  ANR S-ENCODE , the  ARC Fondation  (labelized team and the  CANC'AIR program  Genexposomics) and the  Inserm  Cross-cutting program AGEMED. The B. B. laboratory is supported by the  WELBIO  ( FRFS-WELBIO-CR-2017S-01 ) and the  Fondation contre le cancer  ( FCC / ULB 2018-067 ).
We then quantified the Curie temperatures by fitting two sets of hyperbolic tangent and linear functions to the demagnetization curves. We computed the uncertainties on these Curie temperature using a bootstrap resampling approach where we constructed 1,000 synthetic demagnetization sequences based on the variance of magnetizations around the mean for each temperature and fitted each synthetic data set to find the Curie temperature. To distinguish among different possible Fe-sulfide phases, especially hexagonal and monoclinic pyrrhotite, we obtained thermomagnetic curves of susceptibility as a function of temperature for a bulk Allende sample using an AGICO KLY2 Note. Context and mineralogy are based on optical microscopy. Range refers to the temperatures over which the magnetization unblocks.
8 0. 7 3. 2 and   0. 8 0. 7 4. 2 My after calcium aluminum-rich inclusions (CAIs), respectively (Figure  5 ;  Doyle et al. , 2015; MacPherson et al. , 2017). A more direct constraint on the formation age of Allende Fe-sulfides potentially comes from the 129 I-129 Xe decay system. Early studies of Xe isotopic composition in Allende components showed that the matrix, which hosts the MT remanence, and dark inclusions underwent alteration within 3. 0-4. 1 My of CAIs  (Figure 5; Pravdivtseva et al. , 2003; Swindle et al. , 1983). Later thermochronological reanalysis of Allende I-Xe data suggested that some alteration of Allende material continued until ~40 My after CAIs  (Carporzen et al. , 2011). However, the main carrier phase or phases of iodine in Allende matrix and chondrule material have not been conclusively identified.
Acknowledgments The authors thank  D. S. Ebel  for discussions and providing the Allende sample. Part of this work was performed at the  Institute for Rock Magnetism (IRM)  at the  University of Minnesota. The IRM is a  US National multiuser facility  supported through the  Instrumentation and Facilities program  of the  National Science Foundation ,  Earth Sciences Division , and by funding from the  University of Minnesota. This is IRM publication # 2103. This work was also performed in part at the  Harvard Center for Nanoscale Systems (CNS) , which is supported by the  National Science Foundation  under  NSF  Award  1541959. Development of QDM capabilities supported by  NSF  grant  PHY-1843727  enabled the completion of this work. The authors thank  S. Tikoo  for additional discussions and  G.
This interaction triggers the ATP hydrolysis; the triggering mechanism, however, has yet to be determined  21. HpDnaB belongs to the class of SF4-type, ring-shaped DnaB helicases, and only two crystal structures in complex with ATP analogues have been reported so far, namely the one from Aquifex aeolicus  22  (AaDnaB, PDB accession code 4NMN) Bacillus stearothermophilus  21  (currently Geobacillus stearothermophilus, BstDnaB, PDB 4ESV). AaDnaB is complexed with ADP only and BstDnaB with GDP:AlF 4 -as well as singlestranded DNA, which is similar to our ADP:AlF 4 -complex that we study in presence and absence of DNA herein. W-band electron-electron double resonance (ELDOR)-detected NMR (EDNMR)  [23] [24] [25] [26]  and electron-nuclear double resonance (ENDOR)  27, 28  allow for the positioning of the divalent metal ion within the active site by identifying nuclei in its vicinity.
6  (CP contact time of 1. 5 ms) were obtained using the deposited proton chemical-shift values (BMRB accession code 27879). The hPH spectra reveal intense signals and thus spatial correlations between Pβ of ADP and S206, G208, K209 and T210, all located in the conserved Walker A motif of the P-loop in the motor domain of the helicase  73. Note that for all mentioned amino acids correlations to the backbone amide protons are observed, except for K209 for which additional sidechain Hζ protons are detected. For the Pα resonance of ADP only weak correlations are observed, the strongest one to S211 and an unassigned resonance, Fig. 2  EPR characterizes binding of the metal ion co-factor and ADP:AlF 4 -to DnaB. a EDNMR highlighting hyperfine couplings and thus proximities between the Mn 2+ metal center and surrounding nuclei measured for DnaB:ADP:AlF 4 -(red) and DnaB:ADP (cyan), as well as for a control solution containing only Mn 2+ :ADP:AlF 4 -in the same buffer used for the protein sample (purple).
However, for protons in rather weak hydrogen bonds, the resonances become significantly more shielded upon increasing the temperature, due to an increase in the average hydrogen-bond length. Figure  4  shows the temperature dependence for residues identified in the hPH spectra (left column). Indeed, K209 and T210, previously identified as forming hydrogen bonds to the Pβ of ADP, show an almost vanishing temperature coefficient (slope of the corresponding linear regression). Similar values are found for D374 and G376 The protein resonance assignment is taken from ref. 44  (BMRB accession code 27879). Regular-printed residue labels: Chemical-shift deviation to reported proton shifts <0. 05 ppm. Italic-printed residue labels: Chemical-shift deviation to assigned proton shifts ≥0. 05 ppm. All proton shifts are assigned to amide backbones, except the ones indicated by an asterisk, which are associated to sidechain atoms.
Correlations between the phosphate groups and ADP or DNA are highlighted in green and light red/purple, respectively. The assignments of the DNA proton resonances are based on average chemical-shift values reported in the BMRB database (www. bmrb. wisc. edu). The pink dashed lines highlight signals from insufficiently suppressed DNA in solution. (Supplementary Fig. 7 ) assumed to be involved in DNA coordination. In contrast, Fig. 4  (right column) shows resonances associated with a larger temperature coefficient thus not being involved in hydrogen bonds (for all extracted temperature coefficients see Supplementary Fig. 7 ). Solid-state NMR shows that the AlF 4 -unit is highly mobile. The AlF 4 -unit can be detected in  19  F-and  27  Al-detected MAS experiments. Figure  5a  displays the  19  F MAS spectrum of DnaB:ADP:AlF 4 -in the presence and absence of DNA.
g. via hydrogen bonds to the fluorine atoms) or to the metal ion co-factor. The  27  Al isotropic chemical-shift value of close to 0 ppm is characteristic for an octahedrally coordinated Al-species, in our case most likely formed by four fluoride ligands, one oxygen ligand from the ADP phosphate backbone and one water molecule originating from an "in-line" geometry of phosphoryl transfer  7  or the catalytic glutamate as observed in the BstDnaB structure  21. Homology modelling points to a free rotating AlF 4 -detached from lysine and arginine fingers in SF4 helicases. We performed homology modelling based on the available bacterial helicase structures to investigate whether the dynamic behaviour of the AlF 4 -moiety could be related to the activation mechanism of RecA NTPases. Although the crystal structure of the HpDnaB dodecamer is available (PDB accession code 4ZC0  85  ), its low resolution of 6.
7 Å and the absence of either DNA or of bound nucleotides prevents its use for modelling the ADP:AlF 4 -interactions in the catalytic site of a DNA-bound protein. Therefore, we reconstructed the mechanism of the activation from analysis of the DNA-and Ca 2+ :GDP:AlF 4 --containing BstDnaB structure (PDB accession code 4ESV, resolution 3. 2 Å)  21. In the BstDnaB structure, the Ca 2+ :GDP:AlF 4 -moieties are bound to five out of six catalytic centres (Supplementary Fig. 10 ). Furthermore, the positions and orientations of the AlF 4 -moieties differ among the five catalytic sites (Supplementary Fig. 10 ). By considering these different configurations as mimics of different reaction intermediates, the reaction steps could be reconstructed in the following way: Generally, the interaction with a stimulating moiety enables the nucleophilic attack on the γ-phosphate group by an apically positioned water molecule  17.