dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: This work was partly funded by  IHU Méditerranée Infection, Aix-Marseille-University in France. This work was supported by the  Tunisian Ministry of Higher Education and Scientific Research and Campus France  under the  PHC-Utique  project (Code  18G0819 ) which offered a scholarship to Miss. Raouaa Maaroufi  to attend the  IHU Méditerranée Infection, Aix-Marseille-University in France. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
The beta toxin produced by C. haemolyticum is responsible for bacillary hemoglobinuria affecting ruminants  (Skarin and Segerman, 2014). These virulence genes are all carried on MGEs (plasmids and phages) as part of their mobilome. The Clostridium novyi sensu lato mobilome is highly diverse: More than 60 plasmids or circular bacteriophages (called phages hereafter) categorized into 13 groups were detected upon sequencing only 24 genomes  (Skarin and Segerman, 2014). Considering the few genome sequences available in the public databases, exploring further Clostridium novyi sensu lato MGE diversity is difficult. Yet, clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas), together making up the CRISPR-Cas system, which records MGEs past encounters, could help to shed light on a putative larger mobilome within Clostridium novyi sensu lato.
Genome Selection Fifty-eight genomes (Table  1 ) available in the GenBank database (National Center for Biotechnology Information, NCBI; https:// www. ncbi. nlm. nih. gov/) as of January 2021 were retrieved to explore the CRISPR-Cas systems in the Clostridium novyi sensu lato genospecies. Seven of them were complete genomes  (BKT015925, C-Stockholm, BKT2873, 1873, 3859/11, 150557, and NT)  and the remaining 51 were draft genomes: 45 belong to C. botulinum (BoNT/C, C/D, /D, and D/C), 10 belong to Clostridium novyi, and three belong to C. haemolyticum, showing high variability in sampling year, location, country, sample origin, and genomic lineage. The Clostridium novyi sensu lato genospecies, defined according to a previously published classification  (Skarin and Segerman, 2011) , fall into four different lineages, based on genomic comparisons using Gegenees  (Agren et al.
In silico Analysis of the Spacer Content in CRISPR Arrays Clustered regularly interspaced short palindromic repeats arrays were identified using the CRISPR Recognition tool (CRT), software designed to detect CRIPSR arrays  (Bland et al. , 2007). Settings were chosen so as to have at least three repeats in the CRISPR array with a repeat length between 19 and 38 base pairs (bp) and a spacer length between 19 and 48 bp. The spacer content of each CRISPR array was then extracted manually. Identical spacers were detected in several genomes and were removed to keep only one copy of each spacer that will be called "unique spacer" in the rest of the study. Complementary spacers in the reverse strand or harboring one or two mismatches were considered as unique spacers. Unique spacers were then screened with a Basic Local Alignment Search Tool (BLAST) search to identify protospacers using the CRISPRTarget tool (http://crispr.
otago. ac. nz/CRISPRTarget/ crispr_analysis. html, University of Otago, New Zealand;  Biswas et al. , 2013). Only matches in the GenBank-Phage and Refseqplasmid databases with at least 80% homology were considered. In addition, only the available complete (not draft) genomes were used in these databases. Parameters for the initial BLAST screen were gap open = -10 and extend = -2, minimum BLAST score = 21 with nucleotide match = 1 and mismatch = -2, E value = 1, and Word = 7. Species targeted, target name and type, location in the genome, and gene function (for the main MGEs detected) were reported. Protospacer-adjacent motif (PAM) sequences associated with identified protospacers were reported when identified. In these databases, some MGEs had different names although they were identical (e.
Identification of Protospacers to Trace Past Encounters of Clostridium novyi sensu lato With MGEs The spacers of Clostridium novyi sensu lato CRISPR arrays were also investigated. Spacer arrays indeed represent the memory of the past encounters with MGEs and, by tracing back the spacer origin (the protospacer); they can be used to better understand the mobilome composition/interaction among Clostridium novyi sensu lato strains. Spacer sequences were extracted to build a representative database from the 58 strains used in this study (Table  1 ). We recovered 4,320 spacers, which mostly belonged to C. botulinum BoNT C/D (35. 8%), C. botulinum BoNT/C (20. 2%), and Clostridium novyi (18. 8%), proportional to the number of strains used in our study (Figure  4A ; Supplementary Table  S1 ). Shared identical spacers were investigated first (Supplementary Table  S2 ).
6% of the unique spacer content and C. botulinum BoNT C/D spacers accounting for 20. 6% (Supplementary Table  S3 ). This pattern is in concordance with our above observation on spacers widely shared in the C. botulinum BoNT C/D lineage Ia, resulting in a lower number of unique spacers, with many of them being present multiple times in genetically related strains. We then screened the unique spacers via a BLAST search using the CRISPRTarget tool  (Biswas et al. , 2013)  to determine their origin (the protospacers) and found 474 spacers matching with at least 80% homology in the GenBank-Phage and Refseqplasmid databases (Figure  4B ; Supplementary Table  S4 ). The remaining 1,571 unidentified spacers (Supplementary Table  S5 ) belong to unknown or uncharacterized MGEs, the so-called "spacer dark matter"  (Mcginn and Marraffini, 2019).
A BLAST search on the 474 spacers returned 1,503 protospacer hits in the Refseq-Plasmid and GenBank-Phage databases (Figure  4C ; Supplementary Table  S6 ). Most of the identified protospacers originally belonged to spacers from Clostridium novyi (51. 7%, with 20. 9% located in plasmids and 30. 8% in phages) and C. botulinum BoNT C/D (18. 8%, 12. 6% located in plasmids, and 6. 1% in phages; Supplementary Table  S7 ). The high number of protospacers identified relative to the number of spacer sequences reflects the acquisition of protospacers from homologous genes present in different or related MGEs. The 1,503 BLAST hits retrieved from the identified protospacers corresponded to 776 unique MGEs (Supplementary Table  S8 ), composed of 379 plasmids (48. 8%) and 397 phages (51. 2%). Phages were mostly temperate Siphoviridae and Herelleviridae (among the most common occurring phage species; Supplementary Table  S8 ).
, 2012). Considering that some CRISPR locus located on MGEs can lose their endonuclease, the cascade complex would only be able to bind its target, therefore promoting steric transcriptional inhibition  (Zhang et al. , 2021). Further experimental investigations are required to better understand the role and mechanisms of these incomplete type I-B systems located in MGEs in the biology of Clostridium novyi sensu lato. In addition to type I-B, other CRISPR-Cas system subtypes were found in Clostridium novyi sensu lato strains, except C. botulinum BoNT C/D (lineage Ia) and strains from lineages III and IV. Type I-D was found in C. botulinum BoNT/D (33%) and D/C (90%) and in Clostridium novyi (20%; lineages Ib and II, Figure  1 ). Considering the low number of available BoNT D/C genomes (currently 10 genomes in public databases), it is difficult to draw conclusions and establish generalities.
botulinum group I, II, and III  (Negahdaripour et al. , 2017). This distribution is different from what is commonly reported in prokaryotes, where 85% of spacers usually map to phages  (Mcginn and Marraffini, 2019) , including in other clostridia species such as C. perfringens  (Long et al. , 2019). In addition, some MGEs of Clostridium novyi sensu lato contain proteins characteristic of phages as well as plasmids (e. g. , p1_BKT015925, p1_Cst, p4_BKT015925, and p6_Cst), resulting in misleading annotations in public databases, which may have artificially inflated the proportion of spacers from plasmids. The exploration of CRISPR spacer sequences allowed the identification of their protospacers and thus past encounters of Clostridium novyi sensu lato with MGEs. The hosts from which the targeted MGEs originate provided partial information on the environmental microbiome composition in which Clostridium novyi sensu lato thrived and on the bacterial species with which MGEs are commonly exchanged.
Half of the non-toxic strains did not have any spacers targeting p1_Cst, p1_BKT015925, and neither did six out of the seven lineage IV strains, suggesting no encounter between these strains and p1_Cst and p1_BKT015925, perhaps due to their presence in different ecological niches. This also suggests that these non-toxic strains do not have CRISPR-Cas systems trained against the BoNT phage from the genomes currently available in the database and may therefore potentially be infected by these MGEs. Movements of plasmids and toxin genes across lineage boundaries have been reported in Clostridium novyi sensu lato strains  (Skarin and Segerman, 2014).
Haywood et al. (2020)  prefer to use the NOAA ERSST v5 dataset  (Huang et al. , 2017)  over the HadISST data because of consistency with other observational datasets on a global scale. The Niño 3. 4 moments of the E 280 simulations match reasonably well with those from the HadISST data (see Fig. 2 ). The ensemble mean shows a slightly higher Niño 3. 4 SD (0. 90) compared to the HadISST results (0. 77). The Clim. Past, 17, 2427 Past, 17, -2450 Past, 17, , 2021  https://doi. org/10. 5194/cp-17-2427-2021 (Brierley, 2015). Some models were a part of both ensembles, but note that a different protocol was used in the both MIPs. PlioMIP1 ensemble mean reported a slightly lower SD (0. 78,  Brierley, 2015)  and was compared to the Niño 3. 4 SD of the ERSST data (0. 69). The overestimation of the model Niño 3.
4 SD compared to observational data is also reported in the CMIP5 ensemble  (Kim et al. , 2014). However, there is a good match with the HadISST tropical Pacific leading EOF and that of the E 280 ensemble mean; both in the percentage of variance explained (both 50 %) and correlation of the Niño 3. 4 index with the first PC (both 0. 96). This shows that the PlioMIP2 ensemble does a reasonable job in representing pre-industrial ENSO variability. There is a slight mismatch considering the warm pool (or Central Pacific) to cold tongue (or eastern Pacific) El Niño occurrence ratio (Fig. 6 ). The E 280 ensemble mean shows a considerably higher ratio (0. 80) compared to the HadISST ratio (0. 60). The ensemble mean results do agree with a present-day model ensemble presented in  Yeh et al. (2009)  (approximately 0.
Haywood (a. m. haywood@leeds. ac. uk), as well as more complete data for PlioMIP2 (with exception of IPSLCM6A and GISS2. 1G). PlioMIP2 data from CESM2, EC-Earth3. 3, NorESM1-F, IPSLCM6A and GISS2. 1G can be obtained through the Earth System Grid Federation (ESGF) (https://esgf-node. llnl. gov/search/ cmip6/, last access: 25 February 2021, ESGF, 2020). A selection of the data from the scatter plots presented in the paper is available in the Supplement. The Python scripts (Jupyter Notebooks) used for data analysis are freely accessible through Zenodo (https://doi. org/10. 5281/zenodo. 5718483, Oldeman, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-17-2427-2021-supplement. Author contributions. AMO, MLJB, ASvdH and HAD conceived the presented ideas in this study.
To investigate whether H2A. Bs are reactivated in different cancers, we first used transcriptomic data from The Cancer Genome Atlas (TCGA). This analysis showed that H2A. B paralogs are activated (at a threshold of >1. 5 transcripts per million (TPM)) in numerous individual tumors across cancer types (Fig. 2a , Supplementary Data 1, Supplementary Data 2), but never in adjacent normal tissue (Supplementary Data 1), and very rarely (<1. 5%) in non-testes tissue samples from the Genotype-Tissue Expression database (Supplementary Table  1 ). The range of expression varies widely, with H2A. B-encoding transcripts present at >100 TPMs in two specimens (Supplementary Data 2). Although many tumors reactivate H2AFB1 alone, most tumors that express H2AFB2 also express H2AFB3 (Fig. 2a ). This finding may result from transcriptional co-regulation due to their genomic proximity (Supplementary Fig.
5%) (Fig. 2a ). These same cancers were previously identified as having the highest frequencies of core histone mutations in the TCGA data set, ranging from 5 to 8% 2. We found a few specimens with both recurrent H2A mutations and H2A. B expression (Supplementary Data 4), however, the low numbers of specimens that share both of these features hinder meaningful correlative analyses. Upregulation of H2A. B in HL  23  and DLBCLs (Fig. 2a ) prompted us to analyze data sets from other lymphoid lineagederived, low mutation cancers for aberrant H2A. B expression. We queried four separate B-acute lymphoblastic leukemia (B-ALL) data sets and found 6-7% of specimens with H2A. B-encoding transcripts at >1. 5 TPM (Fig. 2b ) in three of the data sets  [25] [26] [27]  , and 13% in the fourth (Supplementary Fig.
2c )  28. Because of the diversity of liquid and solid cancers with H2A. B expression, we searched the Cancer Cell Line Encyclopedia (CCLE) database  29  for cell lines with H2A. B expression at >1. 5 TPM. Consistent with high-frequency H2A. B expression in TCGA DLBCLs, lymphomas demonstrated the highest percentage of H2A. B-positive cell lines (Fig. 2c ), with 70% of HL and 25% of non-Hodgkin's lymphoma cell lines expressing H2A. B. The spectrum of H2A. B expression across other cancers was also similar between CCLE and TCGA data sets (Fig. 2c ). We conclude that H2A. B is aberrantly expressed in a broad array of cancers. We investigated the potential causes of H2A. B induction in cancer. Although little is known about the transcriptional regulation of H2A. B-encoding loci in testis, changes in Xchromosome ploidy are associated with increased fitness in cancer cells  30.
We investigated whether H2A. B expression in cancer may result from global derepression of large domains, amplifications, or gain of an additional X chromosome in these samples. We compared levels of X-to autosome-linked transcripts in H2A. B-expressing and silent samples and found no significant differences (Supplementary Fig. 2a ). We also investigated the expression profiles of individual H2AFB loci and their surrounding regions and found that upregulation was limited to each individual H2A. B-encoding locus without upregulation of neighboring loci (Supplementary Fig. 2b ). These results are consistent with our findings in the TCGA data set, where median H2A. B expression for the 232 H2A. B-positive samples is ~3 TPM (Supplementary Data 2), corresponding to 49th percentile of all expressed genes.
4a, b ). We also explored splicing in the four B-ALL data sets. Unlike in myelodysplastic syndromes and acute myelogenous leukemias  36  , B-ALLs are not associated with mutations in splicing factors and global splicing dysregulation is not thought to be a major driver of these leukemias. When we compared splicing patterns in the H2A. B-reactivated and silent samples within each data set, we observed aberrant splicing at a scale similar to that seen in H2A. B-positive TCGA cancers, with reductions in alternative exon and APA usage. However, the most notable feature is a consistent decrease in retained introns "ri" in all four data sets (Fig. 4c ). We conclude that H2A. B expression is associated with splicing dysfunction, with some features common among many cancers while others occur in a context-specific manner.
1 gene annotation  53  , and MISO v2. 0 isoform annotation  48. MISO v2. 038 was used to quantify isoform ratios. The trimmed mean of M values method  54  , as applied to coding genes, was used to normalize gene expression estimates across all of TCGA. Data analysis and visualization. Data analysis was performed in the R programming environment and relied on Bioconductor 55 , dplyr  56  , and ggplot2  57. RNA-seq coverage plots. RNA-seq coverage plots (i. e. , Fig. S2c-f ) were made using the ggplot2 package in R, and represent reads normalized by the number of reads mapping to all coding genes in each sample (per million). Somatic mutation analysis. TCGA somatic mutation calls from the Mutect pipeline  58  , were obtained using the GDCquery_Maf function from TCGAbiolinks  59. Mutations in the following canonical histones were collated by their class: H2A-HIST1H2A(A/B/C/D/E/G/H/I/J/K/L/M), HIST2H2A(B/C), HIS3H2A; H2B -HIST1H2B(A/B/C/D/E/F/G/H/I/J/K/L/M/N/O), HIST2H2B(E/F), HIST3H2BB.
The amino acid sequence of human H2A. B. 1. 1 (H2AFB2), H2A. B. 1. 2 (H2AFB3), H2A. B. 2 (H2AFB1), H2A. Q, and H2A. P (HYPM) were examined for short peptides with the potential to bind to common HLA alleles  34, 35. Specifically, the NetPanMHCBA4. 0 algorithm of the Immune Epitope Database and Analysis Resource (IEDB) was used to identify peptides of 8, 9, 10, or 11 amino acids long that are predicted to bind with strong affinity (IC50 < 300 nM) to HLA-A*0101, A*0201, A*0301, A*1101, A*2402, B*0702, B*0801, B*1501, B*1502, B*4001, B*4002, B*4402 or B*4403. Additional IEDB algorithms were employed to confirm predicted HLA binding, whereby binding was predicted by NetPanMHCBA4. 0 and at least one other method (including artificial neural network, stabilized matrix method, PickPocket and NetPanMHCBA4.
0) was required for inclusion in Supplementary Table  5. NCI Genomic Data Commons [https://portal. gdc. cancer. gov/]. RNA-seq reads from B-ALL samples were obtained from the Japanese Genotype-Phenotype Archive (accession number JGAS000047, this data set is available under restricted access and step by step instructions for obtaining access including Form 2 submission are available at [https:// humandbs. biosciencedbc. jp/en/data-use]. For questions regarding this data set, please contact Dr. Hiroyuki Mano at hmano@m. u-tokyo. ac. jp)  27. RNA-seq reads from B-ALL samples were also obtained from the European Genome-phenome Archive (accession number EGAD00001002112  28  , and EGAD00001002151)  26  , and the Chinese Genotype-phenotype Archive (data are available under restricted access, access can be obtained by contacting Dr.
Sai-Juan Chen: sjchen@stn. sh. cn)  25. RNA-seq quantification of CCLE cell lines were obtained from the Broad Institute CCLE Portal [https://portals. broadinstitute. org/ccle/data] (02-Jan-2019 release). TCGA mutation data was obtained through the GenomicDataCommons Bioconductor package. The remaining data are available within the Article, Supplementary Information or from the authors upon request.
Acknowledgements We would like to thank  R. Eisenman  and members of the  Malik, Henikoff, and Bradley labs  for helpful discussions and comments on this manuscript. J. S. is supported by a  Damon Runyon Cancer Research Foundation/Sohn Foundation Pediatric Cancer Research Fellowship , an  Alex's Lemonade Stand Foundation (ALSF)   Young Investigator Award  and a  Northwestern Mutual/ALSF  Award for Data Sharing. A. M. is supported by the  Damon Runyon Cancer Research Foundation  (DRG: 2192-14 ) and by the  NIH  ( R01 GM074108 ). G. -L. C. is a Mahan Fellow. M. B. is supported by a  Stand Up To Cancer   Innovative Research Grant , Grant Number  SU2C-AACR-IRG 14-17. Stand Up To Cancer  is a division of the  Entertainment Industry Foundation. R. K. B. is a Scholar of The  Leukemia and Lymphoma Society  ( 1344-18 ).
Data Analysis We analyzed three-component continuous waveforms of five broadband seismic stations, a part of the permanent seismic network  (Bodvarsson et al. , 1996)  operated by the Icelandic Meteorological Office (IMO). We also used additional data from three seismic stations (ISS, LAG, and LSF) provided by the Czech Academy of Sciences and two stations co-operated with Iceland Geosurvey (LFE, RAH) (Figure  1a ). The data set spans from April 2018 to November 2020 (Figure  S1 ). We performed a visual quality assessment on data to exclude large gaps and clock errors and selected a period (August-November 2019) to construct a reference without significant volcanic/tectonic activity. All seismological data are processed using the MSNoise software package. Our approach is similar to the methodology described in  Lecocq et al.
Acknowledgments We thank the editor and two anonymous reviewers for their comments, which helped us to improve the manuscript. The authors also thank the National Commissioner of the Icelandic Police and the Department of Civil Protection and the Civil-Protection Science-Board team. This study is part of the IS-NOISE project (https:// is-noise. earth/), which is supported by the Icelandic Research Fund, Rannis (https://www. rannis. is/), contract number 185209-051.
Data Availability Statement The authors used the open source MSNoise python package  (Lecocq et al. , 2014)  to process ambient noise seismic data (http://www. msnoise. org/). GMT  (Wessel & Smith, 1998) , ObsPy  (Beyreuther et al. , 2010) , MatPlotLib  (Hunter, 2007) , and Python data visualization tools (https://www. python. org/) are used to prepare all of the figures. ISCE (https://winsar. unavco. org/software/isce) and StaMPS (http://homepages. see. leeds. ac. uk/∼earahoo/stamps/) software were used for the InSAR processing of Sentinel-1 data (https:// sentinel. esa. int/web/sentinel/missions/sentinel-1/data-products and https://scihub. copernicus. eu/). The authors thank IMO technicians/IT department, IMO seismological monitoring team, J. Horálek, and the Czech Academy of Sciences as well as the Iceland Geosurvey for cooperation with station operation.
The authors acknowledge the REYKJANET network (https://www. ig. cas. cz/vyzkum-a-vyuka/observatore/ lokalni-seismicka-sit-reykjanet/). Seismic waveform data  (Bodvarsson et al. , 1996)  is available at http://dx. doi. org/10. 5281/zenodo. 4775293 (Cubuk-Sabuncu et al. , 2021)  and seismic bulletins are available at http:// skjalftalisa. vedur. is/). The authors thank UI, and HS-Orka for providing GPS data (http://brunnur. vedur. is/gps/).
e. , four alternations). The language is recursive and can produce nested descriptions; AABAAB can be described as "two repetitions of [two repetitions plus one change]" (see examples in Fig  1A ). Because of recursion, even long sequences can be encoded compactly in an easy-to-remember form; ABABABBBBBBBABABABBBBBBB is "2 times [5 alternations and 5 repetitions]". The code is available online at https://github. com/sromano/language-of-geometry. Given this language of thought, for each sequence, one can find the simplest expression that describes it, and its associated complexity level (analogous to KC). Complexity is calculated by as a weighted sum of the fixed cost attached to each primitive instruction (+0, and b). As in our previous work  [58] , the additional cost for repeating an instruction n times is assumed to correspond to log 10 (n) (rounded up), i.
In some experiments, in addition to those measures, we also asked participants to report subjective ratings of complexity. Finally, in one experiment, we compared auditory and visual sequences to assess whether our findings would extend to other sensory modalities. For analysis, we examined the correlation between behavioral data and the shortest description length in the proposed language of thought (hereafter called LoT complexity to distinguish it from other complexity measures). To distinguish between rule-based and statistical learning mechanisms, we compared LoT complexity and Shannon surprise as predictors of performance. We started with long sequences of 16 items (experiment 1), and then probed the adequacy of the proposed language to shorter sequences (experiments 2-5). A simple prediction is that shorter sequences are more likely to be stored in a verbatim representation in working memory, without any internal compression.