dataesr/scientific-paragraphs-categorization huggingface dataset:
6 software. For the mcrD gene phylogeny, we used the predicted protein sequences of mcrD in the ANME-1c genomes and in the previously mentioned archaeal database with the TIGR motif TIGR03260. 1 using also the Anvio v. 6 software. The list of genomes from the archaeal database used in the analysis can be found in Supplementary Table  6. For both phylogenies, the protein sequences for the analysis were aligned using clustalw v. 2. 1 with default settings  69. The aligned file was used to calculate a phylogenetic tree using RAxML v. 8. 2. 12 (ref. 64) with the following parameters '-m PROT-GAMMAAUTO -f a -N 100 -k'. The tree was then visualized using iTol  65. For the distribution and phylogenetic analysis of MCP and pPolB, known sequences encoded by various bacterial and archaeal viruses were used to build a Hidden Markov Model (HMM) via hmmer v.
3. 3. 2. The HMM was then used to capture the corresponding components in proteomes of ANME-1 viruses and other MGEs. All sequences were then aligned using MAFFT v. 7. 475 (ref. 70) option linsi and trimmed using trimAl v1. 4. 1 (ref. 71) option gappyout for pPolB and 20% gap removal option for MCP. Maximum-likelihood analyses were carried out through IQtree v. 2. 1. 12 (ref. 72) using model finder and ultrafast bootstrap with 2,000 replicates. The phylogenetic tree was visualized and prepared using iTol  65. For the distribution and phylogenetic analysis of ThyX, all ThyX sequences annotated by EggNOG mapper  73  v. 2 in the genomes of ANME-1 and their MGEs were used to create a HMM as described above, and used to search for close homologues in the GTDB202 database, IMGVR V. 3 database and again in the proteomes and ANME-1 and their MGEs in this study.
MGE annotation and virus identification MGE proteomes are annotated using sensitive HMM profile-profile comparisons with HHsearch v. 3. 3. 2 (ref. 76) against the following publicly available databases: Pfam 33. 1, Protein Data Bank (25 March 2021), CDD v. 3. 18, PHROG and uniprot_sprot_vir70 (9 February 2021)  77. Putative MCP of Chaacviridae and Ixchelviridae could not be identified using sequence similarity-based approaches. Thus, the candidate proteins were subjected to structural modelling using AlphaFold2 (ref. 38) and RoseTTAFold v. 1. 1. 0 (ref. 39). The obtained models were visualized using ChimeraX  78  and compared with the reference structure of the MCP of corticovirus PM2 (PDB id: 2vvf). The contigs containing identifiable viral structural proteins are described as viruses. The remaining contigs are described as unclassified MGEs, including circular elements that are most likely plasmids of ANME-1 and possible viruses enveloped by yet unknown structural proteins.
Genome-scale virus comparisons The viral genomes were annotated using Prokka v. 1. 14. 6 (ref. 59) to produce genbank files. Select genbank files were then analysed using Clinker v. 0. 0. 23 (ref. 79) to produce the protein sequence clustering and alignments. Proteome-scale phylogeny for the head-tailed viruses were carried out via the VipTree server  43. The material type is the genome designated NA091. 008_bin2 (GCA_026134085. 1), a MAG comprising 1. 99 Mbp in 86 scaffolds. The MAG was recovered from mineral sample (NA091. 008) from the hydrothermal environment of South Pescadero Basin.
Acknowledgements We thank  G. Chadwick ,  H. Yu ,  R. Murali  and  A. Philosof  for discussion,  A. Narayanan  for sequencing library preparation,  S. Connon  for technical support and  S. Roux  and  A. Teske  for IMG/VR-related data submission. We are indebted to the science parties and cochief scientists  D. Caress  ( MBARI ),  R. Zierenberg  ( UC Davis ), the crews from  R/V Falkor  (cruise  FK181031 ) and  E/V Nautilus  (cruise  NA091 ) and the pilots of ROVs SuBastian and Hercules. We also thank  R. Speltz  ( Universidad Autónoma de Baja California ) for consultation on species naming from the Kiliwa language. Sample collection permits for FK181031 (25 July 2018) were granted by  la Dirección General de Ordenamiento Pesquero y Acuícola, Comisión Nacional de Acuacultura y Pesca (CONAPESCA: Permiso de Pesca de Fomento  No.
Funding for this work was provided by grants from the  National Science Foundation STC Center For Dark Energy Biosphere Investigations , the NOMIS foundation and by the  US Department of Energy, Office of Science, Office of Biological and Environmental Research  under Award Number  DE-SC0020373  to V. J. O. This work was also supported by the  Deutsche Forschungsgemeinschaft (German Research Foundation ) under Germany's  Excellence Initiative/ Strategy  through the  Cluster of Excellence 'The Ocean Floor-Earth's Uncharted Interface'  ( EXC-2077-390741603  R. L. -P. and V. J. O). M. K. was supported by l' Agence Nationale de la Recherche  grant  ANR-20-CE20-0009-02. F. W. was supported in part by the  Dutch Research.
Data availability Raw metagenome reads, assembled metagenome bins and virus sequence data are available in the NCBI database under BioProject accession numbers PRJNA875076 and PRJNA721962. Complete ANME-1 virus genomes representing new viral taxa can be found on GenBank under accession numbers OP413838, OP413839, OP413840, OP413841, OP548099, OP548100, OP880252, OP880253 and OP880254. CRISPR spacer sequences of ANME-1 and all genomic sequences of ANME-1 MGEs are also provided as supplementary data. For virus genomic analysis the following databases were used in this study: Protein Data Bank (namely, the major capsid protein of phage PM2, PDB id: 2vvf; https://www. rcsb. org/structure/2vvf), CDD v3. 18, PHROG (https:// phrogs. lmge. uca. fr/) and uniprot_sprot_vir70 (09/02/2021).
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy Raw metagenome reads, assembled metagenome bins and virus sequence data are available in GenBank under BioProject accession numbers PRJNA875076 and PRJNA721962. Complete ANME-1 virus genomes from Pescadero basin can be found on GenBank under accession numbers OP413838, OP413839, OP413840, OP413841, OP548099, and OP548100. CRISPR spacer sequences of ANME-1 and all genomic sequences of ANME-1 MGEs are also provided in the supplementary material.
For virus genomic analysis the following databases were used in this study: Protein Data Bank (concretely the protein PM2, PDB id: 2vvf; https:// www. rcsb. org/structure/2vvf), CDD v3. 18, PHROG (https://phrogs. lmge. uca. fr/) and uniprot_sprot_vir70 (09/02/2021).
The GC oven temperature was programmed as follows: 150 • C for 1 min, heated with 5 • C/min to 320 • C and a final isothermal hold of 10 min. Helium was used as a carrier gas at a constant flow rate of 1 ml/min. Mass spectra were recorded with electron impact ionization with a mass range of m/z = 40-550 after a 5 min solvent delay until the end of the GC run time. The transfer line was operated at a temperature of 320 • C, the ion source of 230 • C and the quadrupol at 150 • C. Compounds were identified in the MassHunter Quantitative Analysis software (V10. 0, Agilent Technologies, Santa Clara, CA, USA) based on retention time and comparison with an external series of linear alkanes ranging from decane to tetracontane and manual interpretation of mass spectra following  Nawrot et al. (1994).
Acknowledgments The authors thank two anonymous reviewers for the constructive comments that helped to improve this paper. The authors acknowledge  NASA ,  CNES , their partner agencies and institutions (UKSA,  SSO ,  DLR ,  JPL ,  IPGP-CNRS, ETHZ, IC, MPS-MPG ) and the flight operations team at  JPL, SISMOC, MSDS, IRIS-DMC  and  PDS  for providing SEED SEIS data. This is InSight contribution  277. Jing Shi  was funded by the  CSC  (No. 202106190130 ). IPGP co-authors were funded by the  French Space Agency CNES  and  ANR  fund ( ANR-19-CE31-0008-08 ) and were supported by  IdEx Université Paris Cité   ANR-18-IDEX-0001. Jing Shi  thanks  Dr. Cunrui Han  for his helpful discussion and  Dr. Mijian Xu  for his receiver function code ( Xu & He ,  2022 ).
Data Availability Statement Seismic data of InSight are referenced at http://doi. org/10. 18715/SEIS. INSIGHT. XB_2016. The Mars Seismic Catalog is available at https://doi. org/10. 12686/a17. The receiver functions are calculated by Seispy (https://doi. org/10. 5281/zenodo. 3992424). Figures are plotted by matplotlib  (Hunter, 2007).
e. , at least all pairwise combinations of α and β = 1, 2, 5 and 10, Text S1) in each module (Tables  S5-S7 ). All numerical simulations were run in the packages 'deSolve' and 'rootSolve'  (Soetaert et al. , 2010; Soetaert & Herman, 2009)  in the R software version 3. 6. 2 (R Core Team, 2019).
AC K NO W L E DGE M E N T S We thank Ursula Gaedke, Sebastian Diehl and three anonymous reviewers for detailed comments that helped us improve the manuscript. This research was supported by the Grant Agency of the Czech Republic (Grant no. 21-29169S). NAP was supported by the University of Oulu and the Academy of Finland Profi4 Grant 318930 Arctic Interactions (ArcI). AS was supported by the ANR project EcoTeBo (ANR-19-CE02-0001-01) from the French National Research Agency (ANR). Computational resources were supplied by the project 'e-Infrastruktura CZ' (e-INFRA CZ ID:90140) supported by the Ministry of Education, Youth and Sports of the Czech Republic. The authors have no conflict of interest to declare.
The chosen context is particularly relevant to the teachers' concerns, since inclusive education has been one of the main transformations introduced in France's education system over the last decade (with numerous changes within the past 5 years especially). The debate over the legitimacy of this paradigm (and thus the associated preoccupations) remains particularly vivid in the country (see  Ployé, 2018; Galle, 2021; Jellab, 2021). This study has been pre-enregistered (AsPredicted #90237, see here: https://aspredicted. org/KTL_7G6) and the associated data and materials are fully accessible here: https://osf. io/fsjcu. Frontiers in Education 03 frontiersin. org.
Procedure Data were collected using an online questionnaire sent out to teachers in March 2022 via social and professional networks. Participants were informed that the study was anonymous, that no personal data would be collected, and that they could quit at any time without justification or prejudice. Participants who did not object to participating were then invited to complete the two scales and, finally, socio-demographic data (age, gender, grade level taught, seniority, and inclusive education experience) were collected. At the end of the study, participants were fully debriefed regarding the purpose of the research.
This provides an ultimate, selective perspective that is not always explicitly considered. We survey empirical evidence for different kinds of trade-offs involving defences reported in the literature. This lays groundwork for a section where we discuss theoretical formalizations of defence portfolio evolution. We add novel theory on how traits can interact non-additively to deter predators. We devote the rest of the manuscript to additional factors and mechanisms that could affect the predictions of the basic theory. We consider ecological conditions that may favour the evolution of defence syndromes, and summarize the results of broad phylogenetic studies. Because there is a dearth of such studies, we provide recommendations for assembling and analysing data to test comparative hypotheses.
This may be a necessary step in preparing for a comparative study (Figure  2d ). We encourage researchers investigating defences using phylogenetic methods to consider a workflow like that envisaged in Figure  2 , as it would produce high-quality data applicable to a diversity of questions in antipredator defence. The approach that we have described above would especially yield a dataset tailored to addressing ultimate questions concerning multiple defences. We give five such questions that would provide key advances to our understanding. 1. How does the diversity of defences accumulate over time? Is it possible that multiple defences arise (nearly) simultaneously, as might be expected from diverse and dynamic predator communities, or gradually increase in number, as might be expected under Red Queen dynamics within stable predator-prey systems? Additionally, the number of defences through time could be clustered around a certain threshold or be highly variable.
Stankowich et al. (2014) found no instances of sociality occurring together with noxious chemical sprays in mammals, sug- gesting that these defence strategies may be mutually incompatible, or that they may function against predators in different prey niches (diurnal prey would be exposed to raptorial predators and nocturnal prey to mammalian predators, respectively). In addition to the scarce application of macro approaches to multiple defence in animals, few studies treat defences as a composite trait (but see Stankowich & Carmona et al. (2011)nt defence literature may provide inspiration for future studies of animals. For example, in a meta-analysis,Carmona et al. (2011)collated measurements of genetic correlation between various plant traits and susceptibility to herbivory. They used this dataset, covering 40 species from 19 plant families, to ask which types of traits most strongly predict resistance to herbivores.
We separately defined post-COVID-19 condition as symptoms that persisted for at least 84 days after the initial positive test. We did unsupervised clustering analysis of time-series data to identify distinct symptom profiles for vaccinated and unvaccinated people with post-COVID-19 condition after infection with the wild-type, alpha (B. 1. 1. 7), or delta (B. 1. 617. 2 and AY. x) variants of SARS-CoV-2. Clusters were then characterised on the basis of symptom prevalence, duration, demography, and previous comorbidities. We also used an additional testing sample with additional data from the Covid Symptom Study Biobank (collected between October, 2020, and April, 2021) to investigate the effects of the identified symptom clusters of post-COVID-19 condition on the lives of affected people. Findings We included 9804 people from the COVID Symptom Study with long COVID, 1513 (15%) of whom developed post-COVID-19 condition.
Research in context Evidence before this study We searched PubMed with the keywords ("Long-COVID*" OR "post?covid*" OR "post?COVID*" OR "postCOVID*" OR "postCovid*") AND ("cluster*" OR "endotype*" OR "phenotype*" OR "sub?type*" OR "subtype") for articles published in English from database inception to June 15, 2022. Of the 161 results returned, 24 either provided descriptions of subtypes or proposed phenotypes of long COVID (ie, ongoing COVID and post-COVID-19 condition) or post-COVID-19 condition. In 16 of these studies, phenotypes were manually divided into subgroups; in six of these studies, unsupervised methods were used for patient clustering and automatic semantic phenotyping. The remaining two reports detailed uncommon presentations of long COVID or post-COVID-19 condition. Overall, two to eight symptom profiles (clusters) were identified, with three recurring clusters.
Added value of this study We used a large dataset of prospectively collected self-reported symptoms to identify symptom profiles for post-COVID-19 condition in unvaccinated people infected with the wild-type or alpha (B. 1. 1. 7) variant of SARS-CoV-2 and for vaccinated people infected with the delta (B. 1. 617. 2 and AY. x) variants of SARS-CoV-2. We identified three main symptom profiles-a cluster defined by central neurological symptoms, a cluster dominated by cardiorespiratory symptoms, and a third more heterogeneous cluster showing systemic inflammatory symptoms-which were consistent across variants and by vaccination status. These symptom profiles differed between variants only in the proportion of individuals affected and symptom duration overall.
Study overview and data sources This prospective longitudinal study was based on data prospectively collected in the UK in the COVID Symptom Study (CSS), which were self-reported via an app developed by ZOE (London, UK) in collaboration with scientists from King's College London (London UK), Massachusetts General Hospital (Boston, MA, USA), Uppsala University (Uppsala, Sweden), and Lund University (Lund, Sweden). 13  The CSS is described in the appendix (p 3). Briefly, participants self-reported symptoms (both free text and in response to targeted questions; appendix pp 3-5), SARS-CoV-2 testing and results, vaccination status, hospital attendance, and demographic and comorbidity data. People could also use the app to proxyreport for others, but we have limited our analysis to selfreported information.
The initial list of 20 explicit questions about symptoms was expanded after Nov 4, 2020, to include 34 symptoms in response to evolving knowledge of COVID-19 infection. In addition to data logged through the CSS app, further data were available from a subset of participants who had been recruited to the CSS Biobank. Participants in the CSS Biobank responded to an online questionnaire enquiring about the effect of ongoing COVID-19 symptoms on their daily lives (considered retrospectively), as part of investigations into the effect of the pandemic on mental and physical health. The questionnaire contained 32 questions, split into five domains (medical history, symptoms during the pandemic, lifestyle, individual experiences of long COVID, and life before and after the pandemic). We included responses to a subset of six questions related to the long-term effects of COVID-19 and symptom severity (appendix p 6) in this study to assess the association between symptom profiles and the lives of people with long COVID.
We also invited CSS Biobank participants experiencing post-COVID-19 condition and their families to join a volunteer advisory panel to discuss our research findings and to provide clinical and social context for them. The app and CSS were approved in the UK by Kings College London's ethics committee. All app users provided informed consent for use of their data for COVID-19 research. The CSS Biobank was approved by the UK National Health Service Health Research Authority and licensed under the Human Tissue Authority. CSS Biobank participants were invited to join from the app user base and provided informed consent to participate in the additional questionnaire and sample collection studies, and for linkage to app-collected data. All research was done in full compliance with the Declaration of Helsinki and its updates.
Procedures In this study, we used CSS data reported between March 24, 2020, and Dec 8, 2021, and CSS Biobank data gathered between October, 2020, and April, 2021. We defined people with post-COVID-19 condition as those reporting symptoms for longer than 12 weeks after their first positive PCR or lateral flow test within the study period, as per the UK National Institute for Health and Care Excellence definition. 14  We defined long COVID as reporting symptoms for longer than 4 weeks from the initial positive test. Because we focused on post-COVID-19 condition, we did not formally model the profile of people with long COVID; we merely did descriptive analyses of the demographic profile of those individuals. To study the effect of vaccination on symptom profiling in post-COVID-19 condition, we divided participants into two groups: vaccinated at time of SARS-CoV-2 infection and unvaccinated at time of SARS-CoV-2 infection.
1. 617. 2 and AY. x) variants from May 26 to Dec 8, 2021. We included data for people who had a positive SARS-CoV-2 test (either RT-PCR or lateral flow antigen test) within one of the windows with a predominant circulating strain, had logged that they felt "physically normal" for at least 2 weeks before the test (except for the immediate 7 days before the test), reported symptoms for at least 28 days after diagnosis (symptoms experienced up to 7 days before a positive test were considered symptoms at the onset of the illness), and logged their See Online for appendix For the CSS Biobank see https:// cssbiobank. com/ symptoms at least weekly until they reported feeling healthy for more than 4 weeks (as in a previous study  16  of long COVID in a similar sample; appendix p 8). Further analyses to characterise post-COVID-19 condition included only people whose illness lasted 84 days or longer.
Exclusion criteria were inconsistent non-illnessrelated information (eg, BMI <10 kg/m² or >95 kg/m²), proxy-reporting, being age younger than 18 years or older than 100 years, and reporting a positive test within 7 days of any dose of vaccination, including boosters (because the protective effects of vaccination were unlikely to have begun within this timeframe). 14  The CSS data were split into training and testing sets for symptom cluster optimisation and assessment of the clinical relevance of any observed clusters and cluster reproducibility in a new population. The testing set included 50% of the CSS Biobank population and a matched population (1:1) from the CSS who were not recruited to the CSB Biobank. The training set comprised the remaining 50% of the CSS Biobank population and all remaining eligible participants from the CSS population (appendix p 9).
For people infected with the wild-type SARS-CoV-2 variant, we considered only 20 symptoms (related to the 20 direct questions in the CSS app during the period when the wild-type variant was predominant). For people infected with either the alpha or delta variants, we considered all 34 symptoms for which data were available (appendix p 3). Using an unsupervised time-series clustering approach, we clustered the individuals with post-COVID-19 condition according to their symptom patterns for vaccinated and unvaccinated populations and for each viral variant considered. Sample sizes were sufficient only for analyses of the unvaccinated wild-type, unvaccinated alpha variant, and vaccinated delta variant groups. We estimated the symptom time series per individual by using symptom aggregation per week-ie, we computed the frequency of each symptom per week as the sum of reports during 7 days.
9. 7). The Python package statsmodel (version 0. 13. 1) was used for   The different proportions of vaccinated participants for the various strains are related to the rollout of vaccination in the UK, which began on Dec 8, 2020. 8  We noted no significant differences in the prevalence and duration of symptoms between people who developed post-COVID-19 condition from a SARS-CoV-2 infection (alpha or delta variant) before vaccination and those who developed the condition from an infection after vaccination, suggesting that symptom prevalence and duration among individuals developing post-COVID-19 condition did not depend on the timing of infection relative to vaccination status (appendix pp 12, 17). Overall, we found differing numbers of clusters associated with post-COVID-19 condition per variant, each of which had a specific illness duration and symptom profile (in terms of both prevalence and duration).
Discussion In this study, we profiled phenotypes of post-COVID-19 condition associated with the wild-type, alpha, and delta variants of SARS-CoV-2 on the basis of a longitudinal sample of self-reported symptoms from the CSS. We identified distinct symptom profiles (or clusters), suggesting the existence of heterogeneous profiles of post-COVID-19 condition caused by these different SARS-CoV-2 strains. However, across variants, three groups of symptoms clustered consistently and were reproduced in test dataset with additional outcome data: a primary cluster dominated by central neurological symptoms, a second cluster dominated by cardiorespiratory symptoms, and a third more heterogeneous cluster showing systemic inflammatory symptoms. This Article and our results show the value of digital health applications, such as symptom self-reporting, in the understanding and profiling of a large-scale pandemic.