dataesr/scientific-paragraphs-categorization huggingface dataset:
On the bottom subfigure, another risk threshold concentration c k is estimated for another compound k, with c k > c i. Obviously, the probability p k that the risk threshold concentration for the compound k would be attained is below p i. In the RSDE samples, as the compounds are measured using routine methods, most of the LOQ are quite high. This is not always the case for the other sources of quantified concentrations. Therefore, certain concentration values that lie below the LOQ of the main dataset of the study, i. e. the RSDE LOQ, were obtained from other sources. These values relative to the RSDE LOQ for all compounds could then be scaled in order to have a sufficient number of measured concentrations lying below the RSDE LOQ. This provided a dataset of 141 samples with concentrations denoted ( c j ) 1≤j≤141 issued from 25 different compounds.
Global analysis of characterization factors Regarding the CFs, the different values were reported in the boxplots Fig. 2. Comparison between the CF HT and CF ET (both in log10 scales), for the previously investigated and for the added compounds. For the added compounds, the CFs in orange originated from USEtox® whereas those in blue were computed using  Servien et al. (2022a). of Fig. 2  below. As usual, the CFs are log-transformed with the addition of 1e-10 for the CF HT in order to avoid a computational issue with log 10 (0). 1e-10 was chosen to be less than the minimum of the USEtox® database (5e-9). Fig. 2  points out that the added compounds globally presented a higher CF than those already included in previous studies. More specifically, for the CF HT and CF ET , the maximum, the 1st and 3rd quartile, the median and the mean of the added compounds lay above those of the previously studied compounds.
However, there were also very low CF values that were significantly lower than those from previous studies. All the descriptive values are provided in Table  S4. Extremely high values were also observed for certain compounds already included in USEtox®. For the CF ET , these two strong values concerned the 1,2,3,7,8-PentaCDD (CAS 40321-76-4) and the 2,3,7,8-TetraCDF (CAS 51207-31-9) from the polychlorodibenzodioxin/furane family. Their extreme values could lead to significant potential impacts if their concentrations were not too low. For the CF HT , the extreme value concerns the 2,3,7,8-TetraCDD (CAS 1746-01-6). This is the only value within the whole USEtox® database that is log-positive for this variable. Nevertheless, the estimation of the available CF HT in the environmental footprint database  (Fazio et al.
Estimation of concentrations below the LOQ The following Fig. 3  illustrates the distribution of the measured concentrations from other sources and that lie below the RSDE LOQ. The median (as well as the mean) value of this distribution seems to be close to LOQ/3, and not LOQ/2 as generally used in calculations when data are below the LOQ. Furthermore, since the concentration values below the smaller LOQ were not taken into account, LOQ/3 might even probably be an overestimation, when faced with a value below LOQ in the RSDE dataset. This is also confirmed by the conclusions of  Cantoni et al. (2020)  where the LOQ and LOQ/2 are found to be overestimations.
Conclusion In a previous study, 261 organic micropollutants were selected and their potential impacts on human health and aquatic environments in continental freshwaters were investigated at the scale of France. However, lack of data only allowed for the quantification of the potential impacts for 1/3 of these substances (88 for aquatic environments and 94 for human health). Using a new modelling approach, it became possible to estimate the potential impact of 153 organic micropollutants, i. e. for all associated with an estimated mass released into the environment  (Servien et al. , 2022a (Servien et al. , , 2022b)). The one hundred remaining compounds that did not have an accurate concentration measurement were not taken into account. This implies that their potential impacts were considered equal to zero, even though their characterization factors were high and could be dangerous.
Functional annotation using protein fragments is often ambiguous To investigate the relationship between protein diversity, function and modularity in bacteriophages, we carried out a comprehensive analysis of Hidden Markov Model (HMM) profiles of representative phage proteins by comparing their predicted functional annotations, genetic similarity and domain architectures (see "Methods" and Supplementary Fig. S1 ). Briefly, we used mmseqs2 to cluster 462,721 predicted protein sequences in all bacteriophage genomes downloaded from NCBI RefSeq. The clustering was carried out at 95% coverage threshold to ensure that all proteins grouped within a single cluster have an identical or near-identical domain architecture. We took 133,574 representative protein sequences from the resulting clusters and used them to query the UniClust30 database.
The alignments obtained were then converted into HMM profiles, which we will henceforth refer to as representative HMM profiles or rHMMs. To assign functions to rHMMs, we used hhblits  40  to search each rHMM against the PHROGs database 41 (to our knowledge the most accurate mapping to date between diverse phage proteins and manually curated functional annotations) complemented with a database of antidefence phage proteins  42. Throughout this study, we employed HMM-HMM comparisons to align our rHMMs with themselves or other HMM databases. In HMM-HMM comparisons, two profile HMMs are aligned with each other, capturing the statistical properties of the sequence diversity in each protein family. This involves scoring the likelihood that the amino-acid patterns in one HMM profile match those in another, thereby providing a robust and highly sensitive measure of similarity between representative proteins  39.
We also found that at high pairwise coverage threshold ambiguous functional assignment often reflected biological similarity (e. g. , ribonucleoside reductase vs. ribonucleotide reductase, or transcriptional regulator vs. transcriptional activator; see Supplementary Fig. S3 ). By contrast, at lower sequence coverage thresholds co-occurrences between clearly different functions became more and more common and affected the majority of functions (see Supplementary Fig. S3 ), meaning it was often impossible to confidently assign a function based on a fragment of a protein (i. e. , partial match to a reference database; here PHROGs). Considering this, for further analyses we set the probability and pairwise coverage cut-offs for the PHROG annotations to 95% and 80%, respectively, while conservatively excluding all rHMMs with hits to more than a single functional class (see "Methods").
Proteins assigned to different functional classes share homologous domains Given that for a low pairwise coverage threshold we often found rHMMs to be co-annotated by apparently distinct functional classes, we hypothesised that these functions contained rHMMs that shared homologous domains (i. e. , protein structural and functional units that have been shown to have emerged from a common ancestor). To address this hypothesis, we used the Evolutionary Classification of Protein Domains (ECOD) database as it provides a comprehensive catalogue of known protein domains and their evolutionary relationships  43. We then used HMM-HMM comparisons to detect the presence of these domains in rHMMs (see "Methods" and Supplementary Fig. S1 ). The ECOD database categorises protein domains based on their evolutionary relationships and structural similarities, organising them into a hierarchical system which includes possibly homologous groups (X), homologous groups (H) and topologies (T).
We thus next enquired which cases of ECOD-based mosaicism are ancient (i. e. , represent ancestral domain shuffling underlying functional diversification) and which cases of ECOD-based mosaicism are contemporary (i. e. , are the result of an evolutionarily recent reshuffling of domains). This issue was partially addressed using ECOD T-groups instead of H-groups to assign shared domains in protein pairs. However, to investigate this problem further, we first looked into the sequence similarity distribution of all mosaic pairs of rHMMs and found that only 9% of them shared fragments with a percentage identity of 10% or greater. Then, we reanalysed the data using a definition of contemporary mosaicism by requiring that the shared protein fragments have an amino-acid percentage identity level of 50% or greater.
e. , unique combinations of ECOD T-groups which can be thought of as structurally equivalent proteins). Brown edges connect functions where at least one case of contemporary mosaicism was found (i. e. , a pair of rHMMs with the percentage identity of a shared fragment 50% or greater). Node size corresponds to the number of domain architectures in a given functional class. Edge thickness corresponds to the number of domain architecture pairs with evidence of domain mosaicism. DNA polymerases are shown in Fig. 3. For comparison, in Supplementary Fig. S6  we show an overview of the domain architectures for representative members of all families of DNA polymerases known to occur in bacterial or viral genomes (A, B, C, X and Y)  53, 54  detected using HHpred 55 with ECOD as the database. Our results point to a few notable observations.
Interestingly, a domain-based network of diversity in all lysis genes exhibited a higher level of interconnectedness than for replication and tail protein networks (see Supplementary Fig. S9 ). This phenomenon suggests that endolysin domains likely co-occur in many out of all theoretically possible combinations, which is consistent with previous analyses of domain diversity in endolysins  29, 31, 57. Sequence-based insight points to extensive mosaicism beyond domain analysis ECOD-based mosaicism-our measure of domain mosaicism-serves as a robust approach for detecting protein modularity. It uses the evolutionary information from the ECOD database to discern whether a lack of local sequence similarity is due to evolutionary divergence (same T-groups) or due to a lack of common ancestry (different Xgroups).
The approach also ensures that the units of mosaicism (i. e. , domains) are evolutionarily meaningful as they can fold independently and hence can be horizontally shuffled. However, this approach has two important limitations. The first limitation is that the approach to detect domain mosaicism relies on the assumption that all functional classes have a comparable coverage in the ECOD domain database, which may not be true. Indeed, our analysis of such coverage (see Supplementary Fig. S10 ) shows that while some functional classes-for example, those belonging to the 'DNA/RNA nucleotide metabolism' category-are relatively well annotated by ECOD, other functional classes (e. g. , tail completion, head scaffolding, spanin, holin/anti-holin, nucleotide kinase or tail length tape measure) seem to be strongly underrepresented in domain databases like ECOD.
Domain mosaicism transcends taxonomic and ecological boundaries We next sought to determine whether domain mosaicism is restricted to specific taxonomic and ecological groups of phages. To address this, we assigned taxonomic information (family and genus), host (at the bacterial genus level) and predicted lifestyle (temperate or virulent) to all relevant genomes in the NCBI RefSeq database (see "Methods"). For each genome, we then calculated the proportion of proteins with signal of mosaicism (ECOD-based or sequence-based). This analysis yielded several intriguing observations. First of all, as shown in Supplementary Fig. S14 , our results suggest that temperate phages possess a higher proportion of proteins with signal of mosaicism compared to lytic phages. These findings are in line with the existing literature on the greater genetic mosaicism and frequency of horizontal gene transfer in temperate phages compared to virulent ones  17.
Additionally, the observed differences in proportions of proteins with signal of mosaicism between phage families (see Supplementary Fig. S15 ) and host genera (see Supplementary Fig. S16 ) were statistically significant, with Autographiviridae phages typically having shorter genomes and showing a higher proportion of proteins with signal of mosaicism compared to other families (see Supplementary Fig. S15 ). However, a comparison of the proportion of proteins with signal of mosaicism between multiple groups of phages should be interpreted with caution due to potential confounding factors, most importantly due to the limited ability to detect mosaicism in proteins from phages that are under-represented in databases. We further investigated whether specific phage taxonomic or ecological groups predominantly contribute to the observed domain mosaicism in certain functional classes such as tail spikes or fibres, DNA polymerases or endolysins.
Our analysis revealed that signals of. Blue shows the region with high genetic similarity and information about the percentage identity of that region (amino-acid level). Boxes show detected ECOD domains with their ECOD IDs; homologous domains in a pair have the same colour. (Right). Genomic comparison of the regions where the two proteins were found, with the corresponding names of the phage and genome coordinates. The upper genome fragment corresponds to the upper protein, and stars show the location of the two proteins. Only proteins with informative functional hits (NCBI Genbank) are labelled and marked in green; otherwise, they are grey. Links are drawn between genes with percentage identity of at least 30% across the full length, with the level of identity represented by the scale at the bottom of the figure.
Genome comparison visualisations were made using clinker  97  , and the corresponding NCBI accession numbers are displayed. mosaicism were widespread across multiple phage families (see Supplementary Fig. S17 ). For instance, high levels of mosaicism in tail spikes were primarily observed in the Kuttervirus genus, while mosaicism in proteins related to DNA metabolism was more prevalent in phages with larger genomes, such as Straboviridae and Hellerviridae. Unsurprisingly, when categorising phages as either lytic or temperate, we found that certain functional classes, like those related to integration or transcription regulation, were almost exclusively found in temperate phages (see Supplementary Fig. S18 ). Moreover, endolysins tended to be more mosaic in temperate phages, whereas tail fibres and DNA polymerases exhibited greater mosaicism in lytic phages (see Supplementary Fig.
Discussion In this study, we have systematically analysed the relationship between genetic diversity, functional diversity and protein modularity (detected by instances of domain mosaicism) in phages using 134k Hidden Markov Model (HMM) profiles of representative phage proteins (rHMMs). We compared these profiles to each other and to the ECOD domain database using a sensitive homology search via HMM-HMM comparison. While alternative methods for comparing distant sequences of proteins exist  61, 62  , HMM-to-HMM alignments are known to be the most accurate, able to detect homology even when the sequence similarity falls below 10%  39, 63. Even though there have been recent emerging techniques-such as those employing natural language processing  64  -that show promise in achieving sensitivities comparable to HMM-HMM methods, they have not yet undergone the extensive validation that profile-profile alignment methods have.
Overall, our study not only advances our understanding of phage evolution but also provides a foundational framework that could be exploited for the rational design of phage-derived biotechnological tools. As mentioned before, each of our two approaches to study domain mosaicism has strengths and weaknesses. While ECOD-based mosaicism is a robust approach to detect domain mosaicism, we showed that it is very restrictive and bound to miss genuine cases of protein fragment shuffling (e. g. , horizontal swaps of homologous domains, sub-domain recombination or domain gain/loss), especially in proteins that are under-represented in domain databases. On the other hand, while sequence-based mosaicism is likely to identify these problematic cases, it can result in false-positive cases of mosaicism, for example stemming from highly variable rates of evolution in different areas of proteins.
Given the limitations discussed above, it is to be expected that a great reservoir of mosaicism exists beyond what was reported in this study, namely in (1) proteins of unknown functions, (2) proteins which are under-represented in domain databases and (3) in less frequent, accessory proteins that themselves could have emerged as a result of domain shuffling and diversifying selection acting on the phage pangenome. We thus expect that our results are only the tip of the iceberg which is the true extent of domain mosaicism in phage populations. Our study also raises intriguing questions that extend beyond its current scope but offer fertile ground for future research. For example, our dataset is heavily skewed towards DNA phages, leaving the modularity of faster-mutating RNA phages largely unexplored.
HMM profile construction For each of the clusters, a representative protein sequence was taken as the one suggested by mmseqs2 (i. e. , sequence with the most alignments above the special or default thresholds with other sequences of the database and these matched sequences). Of those, 50 included more than 10 unknown characters and were thus excluded from further analysis. Each of the remaining 133,574 representative sequences was then used as a starting point to build a hidden Markov model (HMM) profile for each of the clusters. The profile was built by aligning the UniClust30 database  93  (release UniRef30_2020_06_hhsuite) against each representative sequence with hhblits with the following parameters: minimum probability = 90%, minimum sequence identity with master sequence = 10%, minimum coverage with master sequence = 30%, and other parameters set to default  40.
Functional annotation To assign each rHMM to a functional category, we used the Prokaryotic Virus Remote Homologous Groups database (PHROGs; version 4)  41. Every rHMM was compared with the PHROGs HMM profile database using hhblits. We used functional classes as defined by PHROGs, but we additionally simplified and merged the names referring to closely related biological functions (e. g. , RusA-like Holliday junction resolvase and RuvC-like Holliday junction resolvase became Holliday junction resolvase; Dda-like helicase and DnaB-like replicative helicase became DNA helicase; head-tail adaptor Ad1 and head-tail adaptor Ad2 became adaptor, etc. ). The exact mapping of used functional categories onto PHROGs is provided in Supplementary Data S1. Only functional classes that (1) were assigned to PHROGs with the total number of at least 500 sequences and (2) were found in at least 20 rHMMs were considered (unless stated otherwise).
Additionally, every rHMM was compared with a database of antidefence proteins 42 using hhblits, and those that had hits to PHROGs and some specific antidefence function were assigned the specific antidefense class. Functional classes were assigned as those with hits to a known class at 80% coverage and 95% probability hit threshold. Finally, rHMMs with hits to more than a single functional class were discarded unless they only co-occurred with generic classes like tail or structural protein.
Domain detection To detect domains in rHMMs, we used the Evolutionary Classification of Protein Domains 43 database (ECOD, version from 13. 01. 2022). Each of the 133,574 rHMMs was compared to the HHpred version of the ECOD database using hhblits with a minimum probability of 20% and otherwise default parameters. Domains were considered as those hits to rHMMs with probability p ≥ 0. 95 and subject coverage scov ≥ 0. 7.
Taxonomic and ecological metadata We first assigned a bacterial host (Genus level) to a phage genome using information from the NCBI database (release October 2022). We used the 38th release of the ICTV classification  95  to assign both Genus and Family. (It is worth noting that many phage genomes lacked assigned taxonomic classifications, particularly at the family level. ) For lifestyle prediction, we employed BACPHLIP 96 (version 0. 9. 6). Phages with a temperate probability of ≥90% were classified as temperate and those with a temperate probability of ≤10% were classified as virulent. Overall, host, family, genus and lifestyle information was assigned to n = 4543, n = 1579, n = 3611 and n = 3375 genomes, respectively. Finally, we assigned host, taxonomic family, genus, and lifestyle to rHMMs involved in mosaic pairs.
Acknowledgements This work was financed by the  Polish National Agency for Academic Exchange  (B. S. , R. M. ), the  Polish National Science Centre OPUS Grant  (grant  2020/37/B/NZ8/03492 ; B. S. , K. S. , R. M. ), the  EMBO Installation Grant  (B. S. , K. S. , R. M. ) and  First TEAM programme  of the  Foundation for Polish Science , co-financed by the  European Union  under the  European Regional Development Fund  (grant  POIR. 04. 04. 00-00-5CF1/18-00 ; S. D. -H. ). We thank  Virkam Alva ,  Carina Büttner ,  Alan R. Davidson ,  Mart Krupovic ,  Andrei Lupas ,  Joana Pereira  and  Eugen Pfeifer  for helpful discussions and/or comments related to this work which was instrumental in finalising this manuscript. The open-access publication of this article was funded by the  Priority Research Area BioS  under the program ' Initiative of Excellence-Research University ' at the  Jagiellonian University in Krakow.
Data availability Genomes used in this study were downloaded from NCBI Refseq in January 2022. Representative HMM profiles were built with UniClust30 release UniRef30_2020_06_hhsuite. Functional annotation was carried out with PHROGs version 4. Domain architectures were predicted using ECOD version ECOD_F70_20200207. All data necessary to reproduce the results have been uploaded to FigShare and are accessible at https://doi. org/10. 6084/m9. figshare. 24004092, with NCBI accession numbers which can be used to link all protein sequences to the corresponding genomes. Domain architecture lookup in different functional classes is available at: https:// bognasmug. shinyapps. io/PhageDomainArchitectureLookup. Source data are provided with this paper.
Code availability Two computational pipelines were used to generate the results: phageprotein-modularity-data and phage-protein-modularity-figures. The first pipeline was written in Python3 and processes NCBI RefSeq data and carries out HMM profile construction, all-by-all HMM comparison and HMM-HMM comparison to PHROGs and ECOD databases. The second pipeline, written in R version 4. 1, takes the output of the first pipeline as input and generates all outputs and figures presented in the publication. Both pipelines are available under the following Github releases: https://github. com/bioinf-mcb/phage-protein-modularitydata, archived under https://doi. org/10. 5281/zenodo. 10021838, https://github. com/bioinf-mcb/phage-protein-modularity-figures, archived under https://doi. org/10. 5281/zenodo.
Oyster sample selection Oyster samples were selected over the five summer seasons between 2011 and 2016 from the entire geographical distribution of POMS disease outbreaks in commercial aquaculture in Australia. Comprehensive data for each sample including location, age, size, mortality rate and viral load are provided in Supplementary data 1, linked by references to scientific publications on these populations. In summary, all samples were collected on oyster farms by researchers from the University of Sydney and all oysters in the final dataset were initially recruited as spat from an OsHV-1 free hatchery in Tasmania (Table  1 ). All samples were tested using OsHV-1 real-time PCR  (Martenot et al. , 2010)  and positive samples containing the highest concentration of OsHV-1 DNA from among the samples collected that day were selected for sequencing.