dataesr/scientific-paragraphs-categorization huggingface dataset:
Reference genome and transcriptome of the common cockle As an initial step in our genomic study of cockle DN, we applied multiplatform DNA sequencing to obtain a reference assembly of the C. edule genome (Fig. S3 ). As our reference specimen, we selected a healthy adult male cockle (Fig. 1B ) carrying a standard karyotype with 19 chromosome pairs. Hybrid genome assembly yielded a chromosome-level reconstruction of the cockle nuclear genome into 19 scaffolds (N50=39. 6 megabases, Mb; Table  S3 ), with an additional 14. 9-kilobase (kb) scaffold containing the mitochondrial genome sequence. Haploid genome size was estimated at 790 Mb, with a G+C content of 35. 6%. We additionally employed RNA sequencing data from seven tissues to reconstruct a 290-Mb reference transcriptome displaying 98. 8% completeness in metazoan gene content (Table  S3 ).
Accordingly, the dN/dS of variants found in only one tumour (private variants) is 1. 00 (Table  S7 ). Additionally, we performed principal component analysis on a set of germline polymorphisms genotyped across the 10 'golden set' tumours and 100 non-neoplastic cockles covering all sampled populations (Fig. S6 ). This analysis split the tumours into two divergent clusters matching CedBTN1 and CedBTN2, and set apart from two non-neoplastic sample clusters representing relatively divergent groups of cockle populations from northern and southern Europe  (25). This result suggests that CedBTN clones are highly divergent both from each other and from modern cockle populations, further supporting two independent origins. Nevertheless, analysis of sequence mapping data shows that the fractions of sequence reads aligning against the C.
Fluorescent in situ hybridization (FISH) probes targeting telomeric sequences showed that, despite such karyotypic plasticity, all the chromosomes in CedBTN cells present a canonical structure (Fig. 4A ). These results suggest that the shifting karyotypes of CedBTN are probably the outcome of extensive chromosomal reorganization and frequent chromosome mis-segregation during anaphase. It therefore seems likely that a fraction of cell divisions may produce inviable cells in these tumours. Next, we inferred copy number (CN) profiles from whole-genome sequencing data for each tumour in our 'golden set'. The profiles were marked by a ubiquitous pattern of highly complex CN alterations along every reference chromosome, with lower CN levels visibly underrepresented (Fig. 4B ). CN distributions were consistent with a modal CN of 4n, suggestive of ancestral tetraploidy, except for one tumour (UGCE17/2401H) presenting a modal CN of 5n.
To further characterize the landscape of somatic alterations in cockle BTN, we applied multiple established algorithms to call structural variants in the 10 'golden set' tumours. We then removed potentially germline events by genotyping these variants on 455 non-neoplastic samples. This approach yielded a conservative set 18,272 high-confidence structural variants (6,916 in CedBTN1, 10,925 in CedBTN2), with deletions being the most frequent type of event (80%, 14,589/18,272; Fig. S17 ). A maximum-parsimony phylogenetic tree reconstructed from these variants confirmed the CedBTN nuclear phylogeny inferred from SNVs, supporting two divergent lineages with a minimal fraction of shared structural variants (Fig. 4C ). Although analysis of dN/dS ratios revealed no evidence of positive selection for post-divergence SNVs or indels in either clone, the availability of CN data offered an additional opportunity to identify potential early cancer-driver alterations.
Satellite DNA expansions illuminate the emergence of CedBTN Finally, we applied a computational method to examine the repetitive complement of the C. edule genome, with a focus on satellite DNA. These repetitive sequences are relevant for genome stability, exhibiting long-term conservation and propensity for rapid copy number changes  (49). Our method identified 34 satellite DNA candidates in the common cockle reference genome (Table  S14 ), four of which varied in frequency between non-neoplastic and BTN genomes, providing further insights into the origins of cockle BTN (Fig. 4E ). Two satellites, named CeS4 and CeS14, were found at high frequency in all samples from a genetically diverse cohort of non-neoplastic cockles, yet were entirely absent from both BTN clones. We designed FISH probes to target satellite CeS4, which confirmed the results obtained from sequencing data (Fig.
4F ). This finding suggests that both CedBTN1 and CedBTN2 may be ancient cancer lineages that diverged from the cockle population before the emergence and expansion of CeS4 and CeS14 in the C. edule germ line. Another satellite, CeS6, was found in cockle populations and CedBTN2 samples, while being absent from CedBTN1 (Fig. 4E ). Lastly, despite satellite CeS31 being exclusive to CedBTN1, our data did not support exclusive presence of any satellite DNA in CedBTN2 samples. These observations suggest that CedBTN2 may have diverged from the cockle population more recently than CedBTN1.
Although our data do not allow estimation of precise ages for cockle BTN, multiple lines of evidence suggest that these clones may have emerged centuries or millennia ago. These include the broad geographic distribution of tumours, the marked genetic divergence between tumours and modern cockles, the recurrent capture of host mitochondria by tumours, and the absence in tumours of satellite DNA elements that are vastly expanded in the cockle germ line. Furthermore, Hart et al. estimate an age of ~500 years for the BTN clone affecting soft-shell clams  (30) , supporting that long-term survival of marine transmissible cancers is possible. Taken together, our findings suggest that CedBTN lineages have undergone a relatively long history of pervasive genomic instability. Studying the mechanisms that enable BTN cells to overcome the effects of such instability will broaden our understanding of the conditions required for cancers to survive and adapt over the long term.
Acknowledgements We thank the fishermen's associations ('cofradías') of Galicia for their advice and assistance with sampling, and the  Galicia Supercomputing Centre (CESGA)  for the availability of informatic resources. We thank  L. F. Møller  ( National Institute of Aquatic Resources, Denmark ),  B. Hussel  ( Alfred Wegener Institute ),  M. Wolowicz  ( University of Gdansk ),  T. Verstraeten  ( Ghent University ),  M. L. Martínez  and  A. M. Insua Pombo  ( Universidade da Coruña ),  C. García de Leaniz  ( Swansea University ),  A. Smith  and  A. Harvey  ( Marine Biological Association, UK ),  R. Parks  ( Centre for Environment, Fisheries and Aquaculture Science, UK ) and  T. Magnesen  ( Universitetet i Bergen ) for providing samples for this project. We thank  C. Canchaya  ( Universidade de Vigo ),  M.
Rey ,  J. Quinteiro ,  M. Hermida  and  P. Martínez  ( Universidade de Santiago de Compostela ) for helpful advice on genome assembly and annotation,  M. Rodríguez  ( Universidade de Vigo ) for administrative support, and  E. P. Murchison  ( University of Cambridge ) for helpful advice and critical reading of the manuscript. Funding: This research was funded by the  European Research Council (ERC)   Starting Grant  no. 716290  (' SCUBA CANCERS '), awarded to J. M. C. T. Sampling research was carried out mainly at the  Universidade de Vigo's Centro de Investigación Mariña , supported by the ' Excellence in Research (INUGA)' Program  from the  Regional Council of Culture, Education and Universities , and co-funded by the  European Union  through the  ERDF Operational Program Galicia 2014 -2020  'A way to make Europe'.
Molecular biology and bioinformatics research was carried out at mainly the  Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) , supported by the  European Regional Development Fund  'A way to make Europe' and the  Research Centre of the Galician University System  (2019-2022). A. L. B. was supported by a predoctoral fellowship from the  Spanish Ministry of Economy, Industry, and Competitiveness  ( BES2016/078166 ), received funding from  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 730984   ASSEMBLE PLUS Transnational Access , and a travel grant from  Boehringer Ingelheim Fonds. M. Sa. was supported by a predoctoral fellowship from the  Spanish regional government of Xunta de Galicia  ( ED481A-2017/299 ). D. G. -S. was supported by postdoctoral contracts from  Xunta de Galicia  ( ED481B-2018/091  and  ED481D 2022/001 ).
S. D. received funding from  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 730984   ASSEMBLE PLUS Transnational  Access. I. O. was supported by a predoctoral fellowship from the  Spanish regional government of Xunta de Galicia Consellería de Cultura, Educación y Universidad  ( ED481A 2021/096 ). J. R. -C. was partially supported by the program to structure and improve research centres (Centros Singulares 2019, CiMUS). T. P. was supported by a predoctoral fellowship from the  Spanish Government  ( FPU15/03709 ) and a predoctoral fellowship from the  Spanish regional government of Xunta de Galicia  ( ED481A-2015/083 ). L. T. was supported by a predoctoral fellowship from the  Spanish regional government of Xunta de Galicia  ( ED481A-2018/303 ). A.
Z. N. was supported by Wellcome no. WT098051. Y. S. J. was supported by a grant from the National Research Foundation of Korea funded by the Korean Government (NRF-2020R1A3B2078973). D. P. was supported by the Spanish Ministry of Science and Innovation, MICINN (PID2019-106247GB-I00 awarded to D. P. ), the European Research Council (ERC-617457-PHYLOCANCER awarded to D. P. ), and the Spanish regional government of Xunta de Galicia. J. D. was supported by a postdoctoral fellowship from the Belgian Research Foundation, Flanders (FWO; 12J6921N). Author contributions: A. Villal. , D. P. and J. M. C. T. designed the project. A. L. B. , M. Sa. , D. G. -S. , S. D. , S. R. , J. Z. , M. A. Q. , I. O. , J. J. P. , J. D. and A. B. -O. developed methods. A. L. B. , M. Sa. , D. G. -S. , S. D. , S. R. , J.
To enable model development, we (i) collected, curated, and integrated the largest publicly available dataset for GPMT; (ii) succeeded in developing externally predictive (balanced accuracy of 70-74% as evaluated by both 5-fold external cross-validation and testing of novel compounds) Quantitative Structure-Activity Relationships (QSAR) models for GPMT using machine learning algorithms, including Deep Learning; and (iii) developed a publicly accessible web portal integrating PreSS/MD models that enables the prediction of GPMT outcomes for any molecules using. We expect that PreSS/MD will be used by both researchers and regulatory agencies to support safety assessment for medical devices and help replace, reduce or refine the use of animals in toxicity testing.
European Chemical Agency (ECHA) dataset Experimental animal data on skin sensitization evaluated with the Guinea Pig Maximization Test (GPMT) were retrieved from the ECHA study results database (https://iuclid6. echa. europa. eu/reach-study-results). Unfortunately, there were numerous problems with the collected raw data. For instance, many numerical data were represented as string variables, the units of measurements were not standardized through the datasets, and there were many "free text" data. Therefore, we extensively cleaned and standardized all the data and converted measurements to the same units in each dataset. We also used regex expressions to find essential features for the database that were described in text format; this was key to classifying endpoints into GHS hazard categories.
Following this laborious data preparation and standardization, we performed both chemical and biological data curation. After removing inconsistent data and non-modelable compounds (see Data Curation section), 1,023 out of the original 5,727 data points were kept. Among 23 duplicate chemical pairs in the dataset, biological annotations for 20 of them were concordant and for three, were discordant, i. e. , duplicative compounds had different annotated classifications (sensitizer vs. non-sensitizer). All the discordant replicates and one of each concordant replicate were removed. The final dataset comprised 995 unique chemical compounds, including 247 sensitizers and 748 non-sensitizers.
Literature We also collected GPMT skin sensitization experimental data from the scientific literature. [19] [20] [21] [22] [23]  After removing mixtures, inorganics, and counter ions, 701 out of the original 745 data points were kept. Only one pair of duplicates showed biological annotation disagreement among 221 chemicals with more than one data point in the dataset. The discordant replicates were removed and only one data point for each concordant replicate was kept. Thus, the final dataset had 374 unique chemical compounds, including 173 sensitizers and 201 non-sensitizers.
Combined GPMT data from ECHA and the literature We merged the curated data from ECHA and the research literature and examined the content of this combined data. There were 41 pairs of replicates between these two data sets, and the sensitization potential of only six of these pairs was annotated differently. These discordant records were removed, and only one record for each concordant pair of duplicates was kept. The merged data set had 1322 unique compounds including 432 sensitizers and 890 non-sensitizers, i. e. , it was imbalanced with the ratio of sensitizers to non-sensitizers of approximately 1:2.
Case studies sets An additional literature search executed identified nine new compounds with GPMT data that were not part of the training set used for model development. These compounds were standardized and used as an additional validation set. We also collected the 474 compounds available in the Extractables and Leachables Safety Information Exchange (ELSIE) Database  24  After the removal of inorganics, mixtures, and duplicates, 415 compounds remained. We found that 102 compounds were present on our GPMT list and 313 unique compounds were kept for model evaluation.
Data curation Datasets were thoroughly curated following the workflows developed by us earlier. 25. First, we performed chemical structure curation and removed mixtures, inorganics, and organometallic compounds, cleaned and neutralized salts, normalized the specific chemotypes, and applied the special treatment to chemicals with multiple replicated records as follows: (i) when replicated records presented the same binary outcome, only one record was kept; (ii) when a majority of replicated records presented the same binary outcome and one had different binary outcome, only one record with the agreeing binary outcome was kept, (iii) when replicated records had different binary outcomes, all of them were removed. All the curated data are available in Supplementary Material.
Model implementation The PreSS/MD web app was implemented on an Ubuntu Server. The app is coded using Flask (http://flask. pocoo. org), uWSGI (https://uwsgi-docs. readthedocs. org), Nginx (http://nginx. org), Python (https://www. python. org), RDKit (http://www. rdkit. org), scikit-learn (http://scikit-learn. org), and JavaScript (http://www. ecma-international. org). PreSS/MD also includes the JSME molecule editor written in JavaScript,  36  supported by the most popular web browsers. Java or Flash plugins are not required to use the app.
QSAR models for predicting skin sensitization using GPMT data High values of MODI (≥0. 7) allowed us to expect that robust and predictive QSAR models could be developed for this dataset. The statistical characteristics of the skin sensitization models built and validated using GPMT data are shown in Table  3. The machine learning models built using RF, SVM, lightGBM, and Deep Learning were able to predict the external set with balanced accuracy of 73%, 74%, 70%, and 72%, respectively.
Case studies As an example of a practical application, we tested PreSS/MD by employing it to predict the skin sensitization potential of nine medical device ingredients identified internally at the FDA with discordant data between GPMT and human clinical data. We compared this list with the dataset used to build our models and found that all these compounds were new and were not included in the original dataset. Therefore, we performed a blind prediction using the PreSS/MD to predict the skin sensitization potential of these nine compounds. The predicted results are shown in Table  4. PreSS/MD correctly predicted six out of nine compounds (balanced accuracy of 65%, sensitivity of 80%, specificity of 50%, PPV of 66% and NPV of 66%). Although the evaluation of these nine compounds presented low specificity, the NPV indicates the probability of predicted non-sensitizer being truly non-sensitizers is high as 66%.
In addition to these nine compounds with GPMT data, we exploited our models to predict a list of 474 chemicals known to leach from MD. After the removal of inorganics, mixtures, and duplicates, 415 compounds remained, and we found that 102 compounds were present in our curated GPMT list. Out of the 313 remaining compounds, our models predicted 98 compounds as sensitizers in the GPMT assay and 215 as non-sensitizers. We analyzed this list's overlap with the expanded skin sensitization dataset of human, LLNA, and three non-animal assays (DPRA, KeratinoSens, and h-CLAT) data described in our previous paper. 14  Out of 313 chemicals, we found that 34 had experimental data in one of the skin sensitization assays. Table  3  shows the concordance of the predicted values using PreSS/MD and the skin sensitization potential available from experimental assays.
14  As seen in Table  4 , 109 compounds were both tested in GPMT and had human clinical data. In total, 46 compounds were sensitizers in both tests and 41 compounds were classified as non-sensitizers in both tests, while 22 disagreed in classification. Therefore, our analysis has shown that the accuracy of using GPMT to predict human skin sensitization is estimated to have the balanced accuracy of 80%, sensitivity of 85%, PPV of 77%, specificity of 74%, and NPV of 84%. Out of the 112 compounds shown in Table  4 , 14 compounds were labeled to be leaching from medical devices in the ELSIE dataset. Of these there were 9 sensitizers and 5 non-sensitizers with human data. All the non-sensitizers in humans were also non-sensitizers in GPMT and only one sensitizer in humans was labeled as a non-sensitizer in GPMT.
In the ECHA dataset, only three pairs of compounds out of 23 duplicate chemicals had discordant annotations. The data collected from the literature had only one pair of duplicates with discordant annotations among 221 chemicals. Finally, there were 41 pairs of replicates between these two data sets, and the sensitization potential was different for only six of these pairs. Conversely, human tests show high inter-individual variability, especially for compounds tested at a high dose, which can show weak sensitization rates in the tested populations. 40  In our previous analysis,  14  we found the accuracy of LLNA to predict Human skin sensitization was estimated to have a balanced accuracy of 68%, sensitivity of 84%, and specificity of 52%. The low specificity means that LLNA is oversensitive to predict human skin sensitization, i.
Thus, the Ochrobactrum and Brucella differences are not limited to their assignments to different "risk-groups", a biologically (and hence, taxonomically) oversimplified description that, moreover, does not support ignoring the nomen periculosum rule, as proposed. Since the epidemiology, prophylaxis, diagnosis, and treatment are thoroughly unrelated, merging free-living Ochrobactrum organisms with highly pathogenic Brucella organisms brings evident risks for veterinarians, medical doctors, and public health authorities who confront brucellosis, a significant zoonosis worldwide. Therefore, from taxonomical and practical standpoints, the Brucella and Ochrobactrum genera must be maintained apart. Consequently, we urge researchers, culture collections, and databases to keep their canonical nomenclature.
In contrast, bacteria with smaller pangenomes have fewer rRNA genes, fewer genes coding for metabolic routes, and a reduced capacity to exchange genes. Consequently, these divergent pangenomes have been depicted as open and closed pangenomes, respectively. Pangenome sizes influence the phylogenetic history in a given bacterial group  [69] , and the pangenome/core genome ratio at the genus level is more pronounced than at the species level  [32]. During the evolutionary history of Brucella, plasmids and prophages were present, and cryptic sequences of these elements have remained in the genome. An investigation of 600 strains of all classical Brucella species and biovars found no free plasmids  [28] , and the prophage sequences detected in the hundreds of genomes of strains of Brucella species in data banks have shown widely different origins.
We can live with this if we understand the context in which taxonomy works. The evolution of pathogenesis (and many other life episodes) is not a bidimensional flat-looking circumstance but a multidimensional emergent process requiring complex and elaborated analysis. In this regard, the Brucella and Ochrobactrum case is paradigmatic. It illustrates how intracellular pathogens of animals diversify from soil bacteria into a totally distinct group, which, as demonstrated, comprises a distinct and clearly defined genus that should not be clustered with Ochrobactrum. As established, Brucella and Ochrobactrum genera must be maintained separately, and we urge researchers, culture collections, and databases to keep their canonical nomenclature. Taxonomy is a valuable and practical endeavor that commonly ends with a Linnaean nomenclature.
Moreover, one of the purposes of taxonomy is to provide names wisely linked to a set of biological characteristics uniformly shared by the living beings under a given designation. For one hundred years, "brucellosis" has been linked to a disease caused by members of the genus Brucella because, regardless of the species, all induce the same syndrome. Before the genus was established, the disease received at least fifty different names, causing significant confusion in the anamnesis and diagnosis  [1]. Therefore, brucellosis is a meaningful name representing a malady caused by a relatively homogeneous group of pathogenic bacteria. From the practical and historical perspective, changing the genus of Ochrobactrum to "Brucella" would alter the mainstream of microbiology laboratories, medical doctors and veterinarians, microbial collections, genome databases, microbiological and medical books, product names, and many other practical issues, mainly targeting low-and middle-income countries where the disease is endemic and/or currently emerging.
Canine Cell Lines The Bear (accession numbers CVCL_OD14) and CML10 (accession number CVCL_IZ11) cell lines were obtained from Dr. J. Modiano (Colorado State University, Fort collins, CO, USA). The cell lines were cultured at 37 • C in RPMI 1640 medium (Gibco, Amarillo, TX, USA), supplemented with 10% fetal bovine serum and 0. 2% primocin (Invivogen, San Diego, CA, USA). Five other cell lines, obtained from fresh canine oral melanoma samples (Dog-OralMel-18249, Dog-OralMel-18395, Dog-OralMel-18333, Dog-OralMel-18848, and Dog-OralMel-18657), were developed in the laboratory (Table  S1 ). These cell lines were cultured in DMEM/F-12 medium (Gibco), supplemented with 10% fetal bovine serum and 0. 2% primocin. After ten passages, RPMI 1640 was used. All cell lines were tested for mycoplasma using the MycoAlertTM Plus kit (Lonza, Rockland, ME, USA), and were found to be mycoplasma-free.