dataesr/scientific-paragraphs-categorization huggingface dataset:
All datasets were plotted seasonally, with oceanographic summer and winter split into two bimonthly periods. Seasonal spawning grounds were defined based on occurrence of egg masses. Regarding identification, we assumed the egg masses had been laid by mature females of whichever species was captured nearby in the same or previous month. The "average" distribution of nursery grounds was defined from captures of paralarvae and early juveniles, and the species identification was inferred, where possible, from the nearest proximity of identified egg masses. Monthly mean seabed temperature data from 2012 to 2020 were downloaded from Copernicus Marine Service Information (CMEMS) (https://marine. copernicus. eu/access-data). Data were imported into R (R Core Team, 2021) and converted into raster files  (Hijmans, 2022).
Loligo vulgaris Mature females of L. vulgaris were present over a much less extensive part of the study area than were L. forbesii, and these were recorded in a much lower number of samples, being restricted to the Irish Sea (few observations), English Channel, and central and southern North Sea. In January-February, mature female L. vulgaris occurred mostly in the western Channel, where they were landed by commercial and recreational fishers, with further observations made on research surveys at several stations in the central and northern North Sea, and with small numbers observed in the landings in eastern Scotland. In March-April, surveys in the western English Channel recorded mature females at many stations and landings also occurred at ports on both sides of the English Channel. Data for May-June were not abundant; mature females were captured in a few research samples coming from the southern North Sea.
Acknowledgements The work was initiated and supported by  ICES Working Group on Cephalopod Fisheries and Life History (WGCEPH). The authors sincerely thanks  Dr Angus Jackson  ( Marine Conservation Society ) and  Dawn Watson  ( Seasearch East ) for helping them to obtain abundant materials on distribution of egg masses collected by Seasearch (https://www. seasearch. org. uk/). The authors wish to thank the  Irish Marine Institute  staff and crew of the RV "Celtic Explorer", as well as cruise leaders, participating staff and  Ingo Wilhelms  from the  Thünen Institute, and scientific staff and crew of RV "Cefas Endeavour" for collection of squid samples ,  Dr Gary Burt  ( Cefas ) for help with data management. Further, the authors want to thank all recreational fishermen and divers who have provided records to the  Cephalopod Citizen Science Project , and  Dr Marta Söffker  (Cefas) and two anonymous reviewers for valuable comments.
The comparative analysis of proteins occasionally reveals examples of natural incongruences between sequence and secondary structure; moreover, it shows that the phenomenon occurred at least occasionally in protein evolution. Figure  2 (top) depicts the alignment of the extant human CYPB1 cytochrome P450 enzyme and its reconstructed ancestral mammalian counterpart, which was recently crystallized (PDB: 6OYU and 6OYV) and characterized functionally  [15]. Despite the high level of similarity of the ancestral and extant folds, the bi-alignment (Fig. 2 , bottom) reveals some differences in the extent of helices and suggests a shift of "helix D" by two amino acids, constituting an incongruence of the considered type. Another published example can be found in Fig. 5  of  [16] : relative to the underlying sequence, one observes several small helix shifts in the evolution of the Pgp protein (MDR1) between human, mouse, and rat.
Computational results We implemented the bi-alignment algorithm with affine gap cost (Corollary 7) in Python 3. For improved performance, we adapted time-critical parts of the code to the Python C-extension Cython with some carefully chosen static typing. The new implementation was based on our previous implementation for RNA bi-alignment with linear gap cost  [12, 23]. Like the earlier version, it allows the user to limit the number of positions either sequence can be shifted to the left or right against its own structure by a constant. The restricted recursions, following M(x 1 , x 2 ) = max    M(x 1 -1, x 2 -1) + s(x 1 , x 2 ) max ℓ≥1 M(x 1 -ℓ, x 2 ) + w(a[x 1 -ℓ + 1. x 1 ]) max ℓ≥1 M(x 1 , x 2 -ℓ) + w(b[x 2 -ℓ + 1. x 2 ]) (12) M �p,ℓ� (x) = M �p,ℓ-1� (x) + d(x, p, ℓ) for ℓ ≥ 1 M �p,0� (x) = max p ′ � =p M �p ′ ,ℓ� (x) (13) in essence the idea of  [34, 35] , have time complexity of O(n 2 2 ) instead of the unrestricted, but often impractical complexity O(n 4 ).
6. A flexible command line and Python interface is included. As a proof of concept we generated optimal bi-alignments of DNA Polymerase I from Escherichia (length 928) and Xanthomonas hortorum (length 933), while allowing shifts of sequence against structure by up to two positions to the left or to the right in either protein ( = 2 ). On an Intel(R) Core(TM) i7-10810U CPU, this (single-threaded) computation took 37 min. Note that a simple banding strategy on insertions and deletions could dramatically speed up such computations, typically without sacrificing alignment quality. The analogous computation allowing only one shift positions ( = 1 ) was performed in 10. 5 min. Due to filling 9 dynamic programming matrices and considering 15 recursion cases per entry, the same implementation still takes 26 s, if shifts are completely forbidden ( = 0).
Results To search for viruses infecting asgardarchaea, we sequenced 12 metagenomes constructed from total environmental DNA directly extracted from the subseafloor sediments originating off the Shimokita Peninsula, Japan (site C9001, water depth 1,180 m)  23  and representing different sediment depths ranging from 0. 91 to 363. 3 meters below the seafloor (mbsf). Asgardarchaeal metagenome-assembled genomes (MAGs) were assembled from 7 of the 12 samples. A total of 20 Asgardarchaeota MAGs, including 12 Lokiarchaeia, 2 Thorarchaeia and 6 Heimdallarchaeia were analyzed in this study (Fig. 1 ), with an estimated completeness of 65. 95±17. 93, estimated quality of 54. 68±15. 78, and GC content of 32. 78±3. 99 (Table  S1 ). To assign the putative viral genomes to asgardarchaeal hosts, we assembled a dataset of CRISPR spacers from asgardarchaeal MAGs assembled in this study as well as those reported previously (Fig.
2 , Table  S2 ). Analysis of the Asgard CRISPR arrays allowed us to define Asgard-specific CRISPR repeat sequences, which were used to identify additional CRISPR arrays from the contigs obtained from our sediment samples (Fig. 2a ; see Methods for details). In total, our CRISPR spacer dataset (Fig. 2b , Supplementary Data 1), included 2,532 spacers assigned to different Asgardarchaota lineages, with Lokiarchaeia contributing the highest number of spacers (Fig. 2c ). All spacers were then used to search for protospacers in the 20 assembled MAGs and putative virus genomes from our dataset as well as contigs from GenBank and JGI sequence databases. In total, 14 contigs could be assigned to asgardarchaeal hosts based on CRISPR spacer matches with estimated false positive rate of 0. 00276 (see Methods for details, Extended Data 1).
By contrast, none of the contigs was targeted by spacers from the CRISPRHost database (675,911 spacers) and only two, likely false positive, spacers were identified in the CRISPR Spacer Database and Exploration Tool (11,674,395 spacers; see Methods). Eight of the putative viral genomes originated from the metagenomes sequenced in this study (from depths ranging from 0. 91 to 87. 7 mbsf), whereas six additional genomes were recovered from datasets sequenced previously, including anoxic subseafloor sediment samples from two Pacific Ocean sites, the Hikurangi Subduction Margin (144. 3 mbsf)  8  and Cascadia Margin (2 mbsf)  24  , and one Indian Ocean site, Sumatra Forearc (1. 6-6. 07 mbsf; PRJNA367446). Each genome was targeted (≥90% identity between spacer and protospacer) by 1-6 CRISPR spacers assigned to putative Asgard classes Lokiarchaeia, Thorarchaeia, and Heimdallarchaeia (Fig.
1 , Tables  S3  and S4 ). Based on the conservation of viral hallmark proteins, including major capsid proteins (MCP), seven of the genomes were unequivocally identified as belonging to viruses from three unrelated groups (Fig. 3 4 5 ), whereas the remaining seven could represent either unknown viruses or other types of mobile genetic elements (MGEs; Fig. S1 , see below). The viral dataset was further enriched by searching the JGI and GenBank sequence databases for related contigs. As a result, we collected 21 contig representing three groups of asgardarchaeal viruses originating from seven geographically remote locations (Fig. 6a ). Further analyses using CRISPR-based (SpacePHARER) and kmer-based (WiSH and PHIST) methods confirmed the association of the 21 contigs with asgardarchaeal hosts (see Methods, Tables  S3  and S5 ).
Caudoviricetes Three of the genomes, VerdaV1, -2 and -3, assembled from the Shimokita dataset, encode the hallmark proteins specific to viruses of the class Caudoviricetes, the most widespread, environmentally abundant and genetically diverse group of viruses  25, 26. Members of the Caudoviricetes infect bacteria and archaea, and have characteristic virions consisting of an icosahedral capsid and a helical tail attached to one of the capsid vertices. Caudoviricetes together with eukaryotic herpesviruses form the realm Duplodnaviria  27. Similar to previously characterized bacterial and archaeal Caudoviricetes  18  , each of the three virus genomes encodes the HK97-like MCP, large subunit of the terminase (genome packaging ATPase-nuclease), the portal protein as well as several other structural proteins, including tail components (Fig.
To this end, we performed BLASTP searches (E-value cutoff of 1e-05) queried with the corresponding MCP sequences against the assembled sequences from samples retrieved along the depth gradient. The analysis yielded six additional viral contigs and showed that related MCPs (and viruses) are also present in sediment samples from 0. 9, 9. 3 and 30. 8 mbsf, indicating a broad distribution of verdandiviruses through the sediment column. One of the retrieved contigs (VerdaV4; 20. 6 kb) was found to be circular and thus also represents a complete virus genome. Furthermore, searches against the GenBank and JGI sequence databases yielded eight hits to virus-like contigs affiliated to asgardarchaea (Table  S3 ). Five of the MCPs were encoded within large contigs (>10 kb) including two (Ga0114925_10000341 and Ga0114923_10001063 [VerdaV5 and VerdaV6, respectively]) circular ones.
Skuldviruses: tailless icosahedral viruses of asgardarchaea One of the contigs from asgardarchaeal MAGs assembled from anoxic sediments from the Hikurangi subduction margin 8 , SkuldV1, targeted by two asgardarchaeal spacers (Table  S3 ), encodes a double jelly-roll (DJR) MCP (Fig. 4a ), a hallmark of viruses of the realm Varidnaviria, an expansive assemblage of viruses evolutionarily and structurally unrelated to duplodnaviruses  27. Varidnaviruses are environmentally abundant and infect hosts from all domains of life  27, 39. Sequence searches with the SkuldV1 MCP led to the identification of two additional contigs, one from our dataset (SkuldV2) and the other one from the GenBank database (JABUBK010000319, hereinafter referred to as SkuldV3). The latter contig was obtained from subseafloor sediments from the Cascadia Margin (Ocean Drilling Program, site 1244) in the Pacific Ocean  24  and is targeted by four spacers from our dataset.
Wyrdviruses are related to spindle-shaped archaeal viruses One of the contigs from asgardarchaeal MAGs assembled from the Hikurangi Subduction Margin 8 , WyrdV1 (15,570 bp), targeted by one CRISPR spacer from our collection, was found to encode two homologs of the MCPs specific to spindle-shaped archaeal viruses  48. In particular, the closest homolog was found in haloarchaeal virus His1 (family Halspiviridae; Extended Data 5)  49. His1-like MCPs are ~80 aa-long and contain two hydrophobic segments predicted to be membrane spanning domains  48  and in all spindle-shaped viruses playing key roles in virion structure and assembly  50. BLASTP searches using the WyrdV1 MCP as a query against the JGI and GenBank databases identified eight additional contigs (Fig. 5 ). All these contigs shared several genes encoding a morphogenetic module, including the MCP and receptor-binding adhesin, which in fuselloviruses and halspiviruses is located at one of the pointed ends of the virion  51, 52.
Assembly, binning, and Asgardarchaeota phylogeny Contigs from each sample were assembled using MetaSpades v0. 7. 12-r1039  70  and binned with UniteM (https://github. com/dparks1134/unitem). Metagenome-assembled genome (MAG) sets were generated after a dereplication of bins using DAS_Tool v1. 1. 2  71 and quality check using CheckM  72. MAGs with an estimated quality (completeness -4*contamination) of less than 30% were excluded in the downstream analysis. Taxonomic assignments of all MAGs were performed using the 'classify' function in GTDBtk  73. The phylogenetic analysis included 20 Asgardarchaeota MAGs, together with 255 published Asgardarchaeota and 64 non-Asgardarchaeota archaeal genomes. A marker set consisting of 53 ribosomal proteins of the 239 genomes were identified and independently aligned using the 'identify' and 'align' functions in GTDBtk  73.
Assembly and analysis of viral contigs Potential viral and MGE contigs were assembled from metagenomic reads by MetaViralSPAdes pipeline with default parameters  77. Direct and inverted terminal repeats were identified by BLASTN  78. Open reading frames were identified with Prokka 79 and annotated using HHsearch  80  against Pfam, PDB, SCOPe, CDD and viral protein sequence databases. Minimum information about uncultured virus genomes (MIUViG)  81  assembled in the course of this work is provided in Table  S7. Read depth along the assembled viral genomes is shown in Fig. S4. To identify similar contigs in public databases we searched sequences of major capsid proteins in GenBank and JGI databases (BLASTP, E-value cutoff of 1e-05, 70% query coverage). Genomes of assembled viral genomes were compared and visualized using EasyFig  82  with the tBLASTx option.
Network analysis of viral genomes was performed using vConTACT v2. 0. 9. 19 with default parameters against Viral RefSeq v201 reference database  83. The virus network was visualized with Cytoscape  84. Collection of the CRISPR spacer dataset CRISPR arrays were detected in metagenomic contigs and 255 published Asgardarchaeota MAGs using minced v0. 4. 2  85  and CRISPRDetect  86. CRISPR arrays from metagenomic contigs were assigned to "Asgard" and "non-Asgard" groups based on CRISPR repeat similarity to previously characterized Asgard CRISPR repeats with 90% identity cutoff over full length of repeat  21. CRISPR repeat sequences from metagenomic contigs and from Asgard MAGs were then clustered together using all-against-all BLASTN search (E-value cutoff of 1e-05, 90% identity, word size 7) and the result of clustering was visualized in Cytoscape  84.
Assignment of CRISPR arrays to Asgardarchaeota To assemble the CRISPR spacer database, we relied on two categories of CRISPR loci: 1) "reference" sequences of manually curated Asgard CRISPR loci from Makarova et al  21  and CRISPR loci from asgardarchaeal MAGs; 2) "metagenomic" CRISPR loci from contigs originating from the Shimokita Peninsula subseafloor sediments. CRISPR loci from the latter category were assigned to asgardarchaea if the identity of CRISPR repeat to the reference asgardarchaeal CRISPRs from the former category was above 90% and protein sequences encoded on these CRISPR-containing contigs (when present) had best BLASTP hit to asgardarchaeal proteins. All "reference" contigs were downloaded from GenBank without annotation. We used prodigal and CRISPR-Cas++ to identify cas genes in the contigs with CRISPR arrays.
Protein sequences were compared to the nr database with BLASTP to confirm Asgard assignment of the contig with CRISPR. Verdandiviruses were targeted by spacers from 5 "reference" and 4 "metagenomic" CRISPR loci (Extended Data 1). The length of "reference" contigs varied from 3 to 126 kbp. Five reference contigs and one metagenomic contig contained cas genes, with the most conserved Cas protein (Cas1) being assigned to asgardarchaea (Lokiarachaeia, Helarchaeia) by the best BLASTP hit with 72-74% identity. Skuldviruses were targeted by spacers from 3 "reference" and 3 "metagenomic" CRISPR loci, whereas wyrdviruses were targeted by spacers from 2 "reference" and 3 "metagenomic" CRISPR loci (Extended Data 1). CRISPR repeats of "metagenomic" contigs targeting the three groups of viruses were 97% identical to the CRISPR repeats from the "reference" category  21.
Asgardarchaeal CRISPR repeats Predicted asgardarchaeal CRISPR arrays contained distinct repeats which could be clustered into 9 groups with 90% identity of sequences within groups (Fig. 2A ). Comparison of the asgardarchaeal CRISPR repeats to those in public RepeatTyper database produced significant results only for group 9 repeat, which was identified as belonging to CRISPR-Cas type I-E. The closest repeat to group 9 was from Thermobaculum terrenum, with 2 mismatches (27/29). Repeats from groups 4 partially matched to Desulfosarcina (29/37). Other repeat groups showed no matches in CRISPR-Cas++ database (https://crisprcas. i2bc. paris-saclay. fr/).
Structural modeling and network analysis of the major capsid proteins Structural modeling of the representative verdnadivirus and skuldvirus major capsid proteins was performed with RoseTTAFold  28. The MCPs of skuldviruses were compared to the DJR MCPs of other known prokaryotic viruses using CLANS  41  , an implementation of the Fruchterman-Reingold force-directed layout algorithm, which treats protein sequences as point masses in a virtual multidimensional space, in which they attract or repel each other based on the strength of their pairwise similarities (CLANS p-values). The reference dataset of DJR MCP was obtained from Ref  42. Sequences were clustered using CLANS with BLASTP option (E-value of 1e-04)  41.
CRISPR-based virus-host assignments Spacer sequences from asgardarchaeal CRISPR arrays were matched to metagenomic contigs and published Asgard MAGs (BLASTN, E-value 1e-05, 90% identity, word size 7), and viral genomes available at IGV database (default parameters) at https://img. jgi. doe. gov/cgibin/m/main. cgi?section=WorkspaceBlast&page=viralf orm. To assess the false positive rate of BLASTN, we used a control set of viral contigs from human metagenomes (~400,000 sequences)  [90] [91] [92] [93]. Seven spacers out of 2532 (FPR = 0. 00276) were matched to a control dataset by BLASTN with the same parameters (E-value 1e-05, 90% identity, word size 7). For each spacer we calculated the difference between identity of best match in the test and control datasets. Spacers with less than 10% identity difference (n=5) were removed as potential low-complexity spacers.
SpacePHARER  94  with FDR rate of < 0. 001 was used for sensitive search of protospacers with lower nucleotide identity to support the BLASTN results. CRISPRHost (https://github. com/davidchyou/CRISPRHost) and CRISPR Spacer Database and Exploration Tool  95  were used to match large collections of prokaryotic spacers to identified Asgard MGE sequences.
Kmer-based virus-host assignments WIsH  96  and PHIST  97   S8 ). For the virus dataset, we used MGE sequences targeted by Asgard spacers. Viral contigs from human metagenomes were used as a negative dataset to calculate null parameters for models of hosts for WIsH. WIsH confirmed asgardarchaeal hosts for all MGE sequences targeted by asgardarchaeal spacers with a Pvalue ranging from 1. 18e-06 for WyrdV1 to 1. 32e-01 for VerdaV2 (Table  S5 ). In the 25-mer-based PHIST analysis, we only considered predictions based on >10 kmers. Using these parameters, hosts were predicted for 17 out of 28 (60%) contigs. Of these, 11 (65%) contigs, representing all groups of viruses and MGEs described in this work, were predicted to be associated with asgardarchaea. However, the remaining 6, all verdandiviruses, were affiliated to Thermoplasmatota MAG 2H_mb2_bin16 assembled from Shimokita peninsula dataset.
We note that the latter prediction likely results from the inclusion of a small (~2 kbp) verdandivirus-like contig into Thermoplasmatota MAG 2H_mb2_bin16, which is likely a binning artefact, because full-length verdandivirus contig was assigned to Lokiarachaeia MAG. Indeed, we could not confirm Thermoplasmatota assignment as a verdandivirus host with CRISPR spacer analysis. To further verify the validity of our CRISPR matches, we checked the predicted asgardarchaeal viruses and MGEs for the presence of non-Asgard protospacers using large spacer collections of CRISPRHost and CRISPR Spacer Database and Exploration Tool (CRISPR_SDET). CRISPRHost did not find any protospacers with the default parameters. CRISPR_SDET search was performed with 90% identity threshold. Two protospacers were found: verdandivirus VerdaV4 was assigned to Floccifex porci, a Firmicutes bacterium from pig gut metagenome; two mobile elements from the "Other" group, JGI-127 and JGI-148, were assigned to Treponema species, a pathogenic bacterium of humans and other mammals.
Acknowledgements We thank the crews, technical staff and shipboard scientists of the DV Chikyu for the operation and sampling during  CK06-06  cruise in 2006. We are grateful to  Miho Hirai  and  Yoshihiro Takaki  for the library construction and sequencing of the subseafloor samples from off  Shimokita. The work in the M. K. laboratory is supported by grants from the l' Agence Nationale de la Recherche  ( ANR-20-CE20-0009-02  and  ANR-21-CE11-0001-01 ) and  Ville de Paris (Emergence(s)  project  MEMREMA ). S. M. was supported by the  Metchnikov  fellowship from  Campus France  and  Russian Science Foundation  grant  19-74-20130. N. Y. and E. V. K. are supported by the  Intramural Research Program  of the  National Institutes of Health of the USA (National Library of Medicine). The work by C.
R and J. S. is funded by the  Australian Research Council (ARC)   Future Fellow Award  ( FT170100213 ) awarded to CR. T. N. was partly supported by  MEXT KAKENHI  Grant Number  JP19H05684  within  JP19H05679  ( Post-Koch Ecology ) and  16H06429 ,  16K21723  and  16H06437  (NeoVirology).
Data availability The raw reads as well as assembled virus and MGE genome sequences from the metagenomes described in this study are available at NCBI under BioProject PRJDB12054, BioSample accessions: SAMD00394285-SAMD00394296. Accession numbers of the 20 MAGs assembled in the course of this study are listed in Supplementary table 1. Extended Data Figure  4  | Sequence alignment of the major capsid proteins of selected wyrdviruses, fusellovirus SSV1 and halspivirus His1. TMD, transmembrane domain.
Furthermore, non-sequence-specific mechanisms of piRNA-mediated gene regulation have also been described  (Shen et al. , 2018; Vourekas et al. , 2016) , showing that piRNAs do not necessarily rely on perfect sequence complementarity to function. These features leave piRNAs as a sort of ''specificity paradox'' in the sequence-based regulation of gene expression, making it difficult to study their direct targets and biological functions. In the C. elegans germline, thousands of highly diverse piRNAs are loaded into the AGO protein PIWI  (Batista et al. , 2008; Das et al. , 2008)  to target and initiate the silencing of transcripts from foreign invasive elements such as single-copy transgenes and TEs  (Ashe et al. , 2012; Bagijn et al. , 2012; Shirayama et al. , 2012). The mechanism of piRNA-mediated silencing relies on an amplification step that requires RNA-dependent RNA polymerases (RdRPs) and components of the Mutator complex to produce secondary antisense small RNAs (called 22G-RNAs) from the targeted transcript  (Bagijn et al.