dataesr/scientific-paragraphs-categorization huggingface dataset:
Raw read QC and selection of viral reads Read quality was assessed using FastQC v0. 11. 8  (Andrews, 2010) , and sequence adapters were removed using trimmomatic 0. 39  (Bolger et al. , 2014). Reads shorter than 50 bp were discarded, and bases at the start and the end of a read were trimmed when their PHRED quality score was below 30. Additionally, reads were clipped if the average quality within a 4-bp sliding window fell below 15. PCR duplicates were removed using Picard v2. 22. 4 ("Picard toolkit," 2019 http://broadinstitute. github. io/picard/ Last Accessed 2022/04/21). Then, viral reads were extracted using KrakenUniq 0. 5. 8  (Breitwieser et al. , 2018)  and Seqkit v0. 12. 0  (Shen et al. , 2016)  with the seq --name --only-id and grep --patternfile. To avoid any host contamination, extracted reads were aligned to a C.
Rarefaction analysis In order to verify that OsHV-1 sequencing depth was sufficient to accurately characterise whole viral genomic diversity, we performed a rarefaction analysis for each of the 21 libraries. To this end, we performed an iterative variant calling analysis by subsampling and mapping the reads on the OsHV-1 µVar A genome (GenBank: KY242785). In order to accelerate analyses, a nonlinear subsampling incrementation was performed. We started by subsampling 1000 reads until reaching 10% of the total number of reads of the library analysed. Then we subsampled 4000, 11000, 21000 and 41000 reads until reaching 30%, 50%, 80% and 100% of the total number of reads of the library respectively. After each step of subsampling/variant calling, variants were compared to the previously identified ones and the new ones were classified in SNPs and InDels in order to increment rarefaction curves.
De novo assembly of non-redundant OsHV-1 genomes Viral reads were used for de novo assembly using SPAdes with the --meta and --onlyassembler options  (Nurk et al. , 2017). Assembled scaffolds were aligned to the OsHV-1 µVar A genome (GenBank no. KY242785. 1) using BLAST 2. 9. 0  (Altschul et al. , 1990)  with an evalue of 0. 00001. Scaffolds were then extended using SSPACE v3. 0  (Boetzer et al. , 2011). Considering that the OsHV-1 genome is composed of a combination of unique (U) and repeated (R) regions (TRL-UL-IRL-X-IRS-US-TRS-X,  (Davison et al. , 2005) ) and that the latter have a size varying from 160 to 9777 bp, most of these repeat sequences could not be resolved using a kmer approach. Scaffolds were grouped according to their size using Seqkit v0. 12. 0  (Shen et al. , 2016). The first group of scaffolds had a size between 2 kbp and 4 kbp and made it possible to select the US region.
Acknowledgments The present study was supported by the  EU  project  VIVALDI  ( H2020 program , no. 678589 ) led by Ifremer; the  CNRS  and the  University of Montpellier. JD was supported by grant from the  University of Montpellier. This work also benefitted from support from the  "Laboratoire d'excellence" (LabEx) CeMEB , through the exploratory research project  HaploFit  and the use of the "environmental genomics" facility (http://www. labexcemeb. org/fr/genomique-environnementale-1,Last Accessed  2022/04/21 ). This work was also supported by the  Ifremer Scientific Board , through the HemoVir and GT-oyster projects and by the " Fond Européen pour les Affaires Maritimes et la Pêche " ( FEAMP ,  GESTINNOV  project  n°PFEA470020FA1000007 ). The authors acknowledge the  Pôle de Calcul et de Données Marines  (PCDM; https://wwz.
ifremer. fr/en/ Research-Technology/Research-Infrastructures/Digital-infrastructures/Computation-Centre ,Last Accessed  2022/04/21 ) for providing DATARMOR computing and storage resources. This study is set within the framework of the  "Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX-41 ). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. JCA, OK, BM, JD and JME were involved in the study conception and design. BM, BP, CP, JD, and JME were involved in the collection of samples and in the experimental work. JD, CP, MJ and JME were involved in bioinformatics and statistical analyses. JD, RG, CM, MJ and JME drafted the manuscript, and all authors revised and approved the final manuscript. We thank the staff of the  Ifremer stations at Argenton (LPI, PFOM)  and  La Tremblade  (ASIM),  Frédéric Girardin  and his team ( Plateforme des Mollusques Marins de La Tremblade PMMLT ) and the  Comité Régional Conchylicole de Méditerranée (CRCM)  for technical support during transplantation experiments.
Data availability The datasets generated from this study can be found in the SRA database BioProject accession number PRJNA681599 with submission ID SUB8642385. The 21 OsHV-1 genomes generated in this study are available in GenBank under the following accession numbers; Brest samples (from OM811577 to OM811581), Marennes-Oléron samples (from OM811582 to OM811588) and Thau samples (from OM811589 to OM811597). All the scripts generated in this study are freely available at https://github. com/propan2one/OshV-1-Downloaded from https://academic. oup. com/ve/advance-article/doi/10. 1093/ve/veac039/6572772 by guest on 26 April 2022.
Access to relevant information is essential for research in all sectors, and because of this we have developed, as part of our COST Action goals, two resources for the free use of all researchers with interests in any aspects of microbial responses to low pH. These are (1) a comprehensive database of references in the literature on different aspects of acid stress responses in different bacterial and fungal species, and (2) a database of research expertise across our network. We invite the community of researchers working in this field to take advantage of these resources to identify relevant literature and opportunities for establishing collaborations.
Two new resources for supporting further research collaborations in the field of low pH responses in micro-organisms As stated elsewhere, understanding how microorganisms respond to acid pH is central to their control and successful exploitation  [11]. When the COST Action was launched, a particular focus was the breadth of the research fields of its members and the extent to which it is hard for researchers (who may be drawn from academia, biotechnology, the food and beverage industry, from clinical and veterinary research and elsewhere) working on their own specialisations to be aware of data, concepts and methods that may be available elsewhere in the field, even though these things could be very valuable to them. With this in mind and with a specific emphasis on building more links between the disparate research topics and industries that members of this Action represent, two new resources were created by the Action for use of the research community: a database of literature in this area and a database of expertise in the Action.
Both of these are ongoing projects in the Action, but they have now reached a sufficient level of development that it is now appropriate to share them more widely. The acid responses literature database developed by Working Group 1. The literature on the impacts of low pH on microorganisms is vast, ranging from empirical observations of how it affects growth through to detailed molecular descriptions of the underlying processes that take place when organisms are treated at low pH. There are many useful reviews on the topic, but no central curated resource existed where literature on different organisms, different acids and different methods could be searched and consulted. Such a resource was therefore created. This was done by using a range of relevant search terms (e. g. acid stress, low pH response, acid tolerance, acid resistance, intracellular pH, pH homeostasis) to interrogate public search engines (Google Scholar, PubMed) for literature on the topic.
Thus far the focus has been on specific groups of microorganisms, using the target genus names as search terms. These groups include the bacterial genera Listeria, Bacillus, Streptococcus, Staphylococcus, Lactocooccus, Lactobacillus, Leptospirillum, Thiobacillus, Acidithiobacillus, Escherichia, and Salmonella. Additionally a number of eukaryotic genera including Saccharomyces, Zygosaccharomyces and Aspergillus, as well as the archaeal genus Sulfolobus, have been included. The intention is to continue expanding the entries to include as many microbial genera as possible that have been studied with respect to their acid responses. Currently (July 2022) there are 566 entries in the database. The inclusion criteria allowed for both research articles and review articles to be included if they contained information that was relevant to the topic.
Articles were included only if they included some fundamental (i. e. molecular/structural/mechanistic) information on the mechanisms involved in sensing or responding to acid stress. Articles describing the effects of either strong or weak organic acidulants were included. Descriptive articles that merely contained information on the growth of microbes in acidic media were not included in the database because it was deemed that they would not help to advance significantly the understanding of how microbes sense and respond to acid at a fundamental level. Including such descriptive articles would have increased the scope of the database to an unmanageable extent and also limited the overall usefulness of the database as a resource for understanding fundamental aspects of the behaviour of microbes under acid stress.
In an attempt to partially quantify the relevance of each entry to the topic (defined as "how microbes sense and respond to acid at a fundamental level") a score was assigned to each article from 1 to 5, where 5 was highly relevant and 1 was minimally relevant. Articles whose entire focus was on understanding the acid stress response scored 5, while articles where acid stress was only a small element of the study scored 1. These scores can be used to filter the entries in the database in order to retrieve the most relevant articles for a particular genus or acid type. The process for constructing this data can be summarised as follows (Fig. 2 ). Articles identified as meeting the inclusion criteria were collected in the freely available bibliographic software Zotero (https:// www. zotero. org/).
The articles were then extracted into a. csv file, which captures all the article-related fields (including authors, journal, date etc. ). Eight additional fields were added to the this file (relevance, genus species identifier, strains investigated, medium/matrix used, pH range investigated, acid type used, date added, person who created entry). The data was then uploaded to Firebase (https://firebase. google. com/), an app development platform backed by Google. This allowed the searchable database web-app to be developed. The firebase platform makes use of various functionalities that were implemented in this database: a. Firebase authentication  [26] : flexible authentication service which can easily be implemented in additional future services connected to this database; b. The Cloud Firestore: a non-SQL database  [27]  contains inner control steps that verify uniformity between new data uploaded into the database and data already present within the database.
In turn, it allows for future scalability of the database and functionalities on top of high speed in performing actions on the data regardless its size; c: Angulara free and opensource web application framework. The database is now publicly available via this weblink: https://costeuromicroph. web. app/. Anyone can access the database, but for a user to sign up they are required to enter a name, email address and password. Thereafter, login requires the email address and password to access the database. The database can be filtered using any of the fields associated with the entries (e. g. Author, Title, Abstract, Strain name, Relevance, Publication Year, Growth Medium/Matrix, pH range, Acid Type used, Genus). Alternatively it can be searched using a user defined search term. The user can also enter articles using an "Add New Documents" feature.
The fields required are clearly defined and users are invited to include details that will provide added value to the database (e. g. strain names, media/matrix type, acid type, etc. ). The newly added articles will be tagged for curation by the database administrator and the entries can be further edited by the curator if needed. The success of the database depends on its use by as many interested end users as possible and on the expansion of the entries to cover as many microbial species as possible. Compendium of expertise in COST Action CA18113 researchers developed by Working Group 2 This COST Action currently has nearly 200 members, the majority drawn from countries in Europe. Members work at universities, companies, research institutes and contract research organisations, working with a wide range of methods on many different organisms and tackling many different research questions.
The conduct of the survey included four stages: 1) Preparation: in this stage the survey was compiled. As the aim of the survey was to find out exact expertise of the people in the field, a set 3) The data were then downloaded from Google forms as an Excel file. Preliminary data curation was performed manually to remove corrupted or duplicated records. Then, the data was gathered, cleaned, curated, consolidated and processed using Python script to facilitate further analysis and to be able to communicate back the data to the action members as raw data. The resulting dataset included 79 records. 4) Data Visualization: For better communicating the results of the survey back to the community, selected data from the dataset was visualized using the online freeware visualization tool Tableau software (https://www.
tableau. com [28] ). Eventually an interactive dashboard that enables visualization of the survey data was launched (example outputs are shown in Fig. 3 ). The dashboard includes the distribution of EuroMicropH's network, affiliations, activities, environment parameter, acid of interest, type of fermentation and relation with industry. These results are presented as a Tableau visualization webpage and can be accessed online here: https:// euromicroph. eu/network/, or on the "Network" tab of the web-site, euromicroph. eu. The visualization also gives some insights related to the network. For instance, EuroMicropH's network is spread across Europe, with particular strength in Poland, Serbia, Turkey, Portugal and Spain (Fig. 3A ). Many people in the network come from academia (Fig. 3B ). The commonest area of research or activity is in Food/ Food Safety (Fig.
3C ), mostly dealing with acidic pH particularly with organic acids, reflecting their importance as preservatives (Fig. 3D ). There are some gaps in the fermentation field, especially in chemical production (Fig. 3E ). Moreover, EuroMicropH's network has partners in industry, mostly in dealing with bioreactors, food safety and microbiological control. All of these data can be found in the link and can be filtered directly by clicking the figure. There are also some further filters that are available on the top of the dashboard. These filters can also be used to find specific expertise in the field. For example, the filter could be used to find whether Euro-MicropH's network has an expert that works in energy, dealing with microbial communities with fatty acids as the focus of their research.
Phenotypic data Traits recorded at the plot level. Heading date (HD) was measured when 50% of the spikes headed. The number of spikes per meter square (NSM2) was determined after anthesis, by manual counting of spikes on a 1 m long transect in the central row. After harvest, the yield of each mixture (Y) was computed as the raw weight of harvested kernels, measured at a 0% of humidity (humidity-meter, TM, Tripette and Renaud, France). It was corrected according to the number of some missing rows due to cropping aleas. Y is finally given in grams per m 2. The whole plot harvest was divided by half with a seedburo divider followed by second division by 8 using a precision Retsch divider. A final count of 1200 kernels from a sample was done on a Contador device (Pfeuffe) and then precisely weighed.
We counted the number of positive or negative HE slopes for each trait and each water treatment. We assessed whether diversity had a rather positive or negative effect on the trait by testing whether there was a significant difference in the number of positive and negative slopes by a Wilcoxon test. In a second step, we examined the stress gradient hypothesis (HE has a more positive effect in stress environment, i. e. , higher under CWD than under R) by testing whether the slopes under CWD were on average higher than under R, using a paired Wilcoxon test on the paired differences in slopes computed across treatments for each SNP. All data and scripts can be found at https://doi. org/10. 15454/FG261F/I7OJKU.
We formalized the distributed, multisystem perspective by testing patterns that crossed resting-state modular boundaries, including a priori multisystem maps based on prior literature and the whole brain. Formal model comparisons at each spatial scale quantified the extent to which modular features, distributed networks, and the whole brain represented participants' pain report. Finally, we repeated this process across a selection of datasets with varying data acquisitions and pain manipulations, generalizing our findings to novel studies and contexts. com/canlab/canlab_single_trials). One additional dataset is used for validating model specificity to pain vs. non-pain stimuli. That additional dataset can be found here: https://neurovault. org/api/ collections/3324/. Finally, all numerical data underlying figures in this manuscript are available through github at https://github.
Behavioral data analysis: Pain report is multidimensional First, we characterized how well our dataset reflects thermal pain experience across diverse task contexts. All but one study (Study 6) varied stimulus intensity and duration (41. 1˚C to 50˚C, 1 to 11 seconds), and all but one study (Study 5) had psychological manipulations of pain (pain-predictive expectation cues, Studies 1 to 4; social cues, Study 7; romantic partner hand-holding, Study 6; and perceived control, Study 4). We used linear models of pain report to quantify the cumulative contribution of these factors. Together, they explained 51% of overall variance in pain across studies (balanced subsample of data, n = 112 participants, Fig 1B  , S1  Table ) and 71% of within-participant pain variance specifically. However, this varied substantially between studies: In some cases, psychological factors primarily drove pain variation (Study 2), and in others, sensory factors like thermal stimulus intensity were the most important  (Study 5).
A psychological manipulation designed to affect pain intensity preceded the stimulus in many designs, for example, a painpredictive cue designed to heighten or reduce pain experience. Thermal stimuli varied in intensity and were mostly delivered to the left ventral forearm as illustrated. Cues varied across studies and were uncorrelated with stimulus intensity. After each stimulus, participants rated their pain intensity using a visual scale. For details on each design, refer to Table  2. (B) Pain ratings reflect a variety of influences on pain experience. Wedges show the proportion of variance explained by each factor for each study individually and across all studies (excluding validation studies, balancing participants across studies). Since fMRI data were quartiled for computational tractability, and z-scored for between study normalization, these data were also quartiled and (in the aggregated data) z-scored prior to regression analyses.
The sum of wedges equals model variance explained. Residual model error is not shown, but completes the circles. We do not consider between-pAU : Pleasenotethatasperstyle; thetermsubject=sshouldnotbeusedforhumanpatients:Hence; al articipant differences further in this study, but we display them here to better characterize nonexperimental sources of pain variability. See S1 Table for standardized regression coefficients. Underlying data: https://github. com/ canlab/petre_scope_of_pain_representation/tree/main/figure1. Illustrated arm adapted from Da Vinci's Vitruvian man  [101]. fAU : Abbreviationlistshavebeencomp MRI, functional magnetic resonance imaging. https://doi. org/10. 1371/journal. pbio. 3001620. g001 pain varied according to variations in study designs and were representative of a swath of the evoked thermal pain experience that spanned both sensory and psychological dimensions.
05, twice repeated 5-fold cross-validated predictions modeled with random participant and random study effects, S ˇidak correction for multiple comparisons, effective p < 10e-4). This means the "best" region, cRSN or fRSN, is highly dependent on the participant sample examined (and will differ across folds in a CV scheme). Regions are color-coded by large-scale structures (i. e. , subdivisions of Ctx, Cblm, Bstem, or Thal). cRSNs and fRSNs are colored by individual network parcels. Underlying data: https://github. com/canlab/petre_scope_of_pain_representation/tree/main/figure3. Cblm, cerebellum; cRSN, coarse resting-state network; Ctx, cortex; CV, cross-validation; fRSN, fine resting-state network; Thal, thalamus. https://doi. org/10. 1371/journal. pbio. 3001620. g003 coefficients for all "best" areas selected during cross-validation (CV; maximum 10 areas, from 2 × 5 CV) and took the union of significant pattern weights for the modules selected across folds, constructing one map for each spatial scale  (3 maps, Fig 4).
When fit to the complete dataset (7 studies), the best modules were right anterior cingulate (a24pr), right salience/ventral attention, and bilateral frontoparietal networks, respectively. However, different CV folds chose different "best" modules (Fig 4  , barplots ). The regions selected appeared to be sampled from areas implicated in sensory processing and orienting responses and were overrepresented in the right hemisphere. Thus, CV predictions from modular spaces reflect the same uncertainty in selecting a single "best" module as we described Multivariate brain models of pain response can be interpreted as nested models that draw on increasing amounts of unique information. Which module-single-region, fine-scale (fRSN) and coarse-scale (cRSN) restingstate network-was selected by the algorithm varied across folds.
Across all 6 model spaces, we commonly observed frontal midline deactivations, and cingulate, insular and cerebellar activations, supporting a nested modeling space interpretation. Underlying data: https://github. com/canlab/petre_scope_of_pain_representation/tree/main/figure4. cRSN, coarse resting-state network; CV, cross-validation; fRSN, fine resting-state network. https://doi. org/10. 1371/journal. pbio. 3001620. g004 above (Fig  3 ). In the full brain modeling space, significant pattern weights (bootstrap p < 0. 05) were found in ACC, insular, and visual cortices; PAG; brainstem cuneiform, parabrachial, and raphe nuclei; select cerebellar lobules; and pulvinar, VPM, and ventrolateral nuclei of the thalamus (significant percent voxel coverage in constituent regions relative to permuted null distribution of shuffled voxel indices, α = 0.