dataesr/scientific-paragraphs-categorization huggingface dataset:
In-depth phylogenetic analyses of individual hits from O. mediterraneus strain RCC2572 (MMETSP0929), O. lucimarinus strain BCC118000 (MMETSP0939), Micromonas MMETSP transcriptomes  (1084 , 1327 , 1387 , 1393 , 1400 , 1401 , 1402 , 1403 , 1404 ), and Mantoniella MMETSP transcriptomes (1106 , 1468)  were performed as previously described for the reference genomes. The presence/absence matrix of each informative orthologous group against the transcriptomes was generated using the ggplot2 package in R  (Wickham 2011). To validate and elucidate each MMETSP transcriptome's taxonomic affiliation, we downloaded Mamiellophyceae 18S rDNA sequences from reference genomes in GenBank, the SILVA database  (Quast et al. 2013) , and Micromonas spp. sequences provided in  Simon et al. (2017)  (supplementary table  S6 , Supplementary Material online).
Transcripts matching selected 18S sequences were extracted with BLASTN (maximum E-value 1e-5) and 18S rDNA sequences were subsequently predicted using RNammer  (Lagesen et al. 2007). A ML phylogenetic tree was built using IQ-TREE and following each clustering of this Mamiellophyceae reference tree (rooted in Monomastix spp. ), transcriptomes were tentatively classified according to a species clustering (supplementary fig. S2 , Supplementary Material online). Phylogeny indicated that MMETSP transcriptomes matched their species classification, and transcriptomes from novel Micromonas species as M. polaris and M. bravo were designated using the data and new classification of  Simon et al. (2017).
Synonymous and Nonsynonymous Divergence of Shared MT GFs We used homologous pairs of the 69 shared MT GFs to calculate sequence genetic divergence with the seqinr package v3. 4-5 (kaks function) using  (Li 1993 ) method (LWL85) in R. 2015-675752 to L. F. B. and F. B. ). We thank the Moore foundation for sequencing most of the Mamiellophycean transcriptomes analyzed in this study and the Genotoul Bioinformatic platform for providing computing and data storage resources.
Acknowledgments This work was funded by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie ITN  project  SINGEK  ( H2020-MSCA-ITN-Evolutionary Genomics of Sex-Related Chromosomes GBE Genome Biol. Evol. 13(10): doi:10. 1093/gbe/evab216Advance Access publication 1 October 2021.
Data Availability All genomic and transcriptomic sequence data have been deposited at GenBank under the accession numbers CAID00000000. 1 (Ostreococcus tauri), PRJNA337288 (O. lucimarinus), PRJNA15676 (Micromonas commoda), PRJNA15678 (M. pusilla), PRJNA394752 (Bathycoccus prasinos), and PRJNA248394 (MMESTP). The accession numbers of the 18S rDNA sequences are summarized in supplementary table  S6 , Supplementary Material online.
Thus, we contacted different actors (i. e. cocoa producers, a cocoa national association, small retailers; Table  1 ) who helped us to understand the local dynamics and vocabulary, as cocoa production is rooted in the indigenous culture and specific terms are used in the field. This step was crucial for developing our research protocol. Secondly, eight companies confirmed their interest and contributed with interviews and, in some cases, documentation. The number of cases, as in previous studies (Le  on-Bravo et al. , 2020; Rodríguez et al. , 2016; Silva et al. , 2018) , is considered appropriate, as the phenomenon and the industrial setting studied are both underexplored in the literature. We used two main techniques to gather datasemi-structured interviews and secondary data collection. The interviews were used to gather general information about each company and learn each addresses sustainability, the process of changing the logic to value-adding along with a food commodity SC and how the organic certification schemes help to develop and improve sustainability along with their operations.
As noted, the interview protocol was built based on input from different actors in the chain and it was completed using definitions from the literature regarding SSM processes, sustainable production practices, traceability systems and drivers for and barriers to achieving organic certification. Secondary data collection involved data gathering and validation from company websites, internal documentation (when provided) from industrial associations, websites and newspapers and from the website of the Ecuadorian national certification organisation. As shown in Table  1 , the cases analysed comprised six organic certified cocoa processing companies (i. e. chocolate producers) and two retail companies that specialised in organic artisanal products coming from smallholders, fair trade or cooperatives around Quito, the capital city of Ecuador.
Interviews were conducted in Spanish in person or by phone, depending on availability and access to the company locations; each interview lasted 20-60 min. To prevent mistranslation, back translation of the data (i. e. from Spanish to English and back to Spanish) was conducted by two of the researchers. In addition, secondary data were collected, mostly in Spanish but, in some cases, in English, from various sources such as webpages and videos from companies, associations and cooperatives. Collected data were coded in two steps following a groundup approach (inductive analysis). To increase reliability and rigor, the analysis was double-checked by the authors in several rounds, which ensured transparency of reasoning  (Ketokivi and Choi, 2014; Seuring, 2008). As our first step, we identified the activities related to sustainability that each company deployed.
Impact Statement Streptomyces are bacteria that, unlike most others, have a large and linear chromosome that consists of a central region flanked by two variable arms. Our work aims at understanding the evolutionary forces that are imposed on this chromosome and have shaped this compartmentalization over evolutionary times. For that purpose, we established a set of 125 Streptomyces chromosomes from public databases and monitored indexes measuring the conservation of genes (presence/absence) and their organization (synteny) along the chromosome. Using the phylogenetic depth offered by the chosen panel, it was possible to infer that within the central region of the chromosome, the core-genes form a highly conserved organization, which can reveal the existence of an ancestral chromosomal skeleton.
Streptomyces genus The 234 complete Streptomyces genomes available in the NCBI database  [27]  as of 17 January 2020 constituted our basic genomic data set. First, the completeness of the assembly of the genomes retained in the dataset was assessed using BUSCO  [28]  with the streptomycetales dataset (odb10). Second, to avoid redundancy, the genomes were clustered using their ANIb value and genomes sharing a value greater than or equal to 96 % were considered to represent strains belonging to the same species  [29] ; therefore only one of them was retained in the final dataset. As an exception, Streptomyces ambofaciens DSM 40697 and ATCC 23877 were both kept in our dataset as they represent the model species of our laboratory, and allow a comparison of closely related strains. In total, the complete genomes of 125 strains or species were collected (Table  S1 ).
Terminal inverted repeats detection and identification Terminal inverted repeats (TIRs) are defined as perfect repeats of DNA at the ends of chromosomes or linear plasmids. However, these repeats may or may not be present in the assembly published in the database depending of their size, which can vary widely (from a few tenths of nucleotides to several hundreds of kilobases) and the sequencing technologies used (i. e. sequencing from recombinant BACs or cosmids, whole-genome sequencing using long or short reads). The longer the repetition is, the lower is the chance of identifying it as a single piece in a sequence scaffold. To detect the presence of TIRs and to identify their size, we searched for DNA repeats at the ends of all the considered chromosomes. To do this, by using blast, we searched for an alignment between the two chromosomal sequence ends (blastn v2.
Genome annotation, orthology assignment All genome sequences were automatically annotated on the rast Server  [30]  using the rast Classic pipeline (FIGfam version: release 70) to provide a homogenous annotation process, and fed the orthology assignment process with a valid and consistent annotation dataset. For each pair of genomes, orthologues were defined by identifying blastp reciprocal best hits (BBH)  [31, 32]  with at least 40 % of identity, 70 % of coverage and an E-value lower than 1e -10.
Core-and pan-genome computation The core-genome was determined as the set of orthologues present in all the genomes of a given dataset, and the pangenome as the sum of the core-genome plus the specific genes found in the genomes of the same dataset. Both growing sets were computed for genome sets ranging from 2 to 125 genomes. For any set of size n smaller than 125, core-and pan-genomes were computed by performing 100 iterations of a random selection of n different genomes from the 125 genomes available in our dataset. The core-and pan-genomes size for each dataset size can be represented as a box-and-whisker plot with the lower and upper whiskers corresponding to the first and the last quartile of the distribution. Based on the position of the genes belonging to the core-genome, the core-region has been defined as the area including 95 % of them to avoid overestimation of the extent of the core-region (as illustrated for S.
Phylogenetic inference based on maximumlikelihood For each genome, the protein sequence of the 1018 genes of the core-genome were retrieved. After alignment using muscle v3. 8. 31  [33]  and trimming by Gblocks to eliminate poorly aligned positions  [34] , the 1018 multiple sequence alignments were concatenated into a unique multiple sequence alignment comprising 324 451 amino acid positions. The multiple alignment was then submitted to RAxML  [35]  with the LG substitution model for maximumlikelihood-based tree inference. One hundred bootstrap replications were performed. The phylogenetic tree was represented using the Dendroscope software  [36].
In other words, a window in a poorly conserved region will extend over a larger chromosomal area than a window in a more conserved region. Further, depending on the phylogenetic distance between the compared strains, a common chromosomal locus will be included in windows of different sizes. As a consequence, the trend of the NOC cannot be plotted onto a physical map of the reference strain and therefore cannot be compared to the OR and GOC trends. Finally, both GOC and NOC indexes turned out to be complementary and relevant to assess genome plasticity across evolutionary times. GOC and NOC analyses were performed for all of the 15 500 pairwise comparisons of species of our dataset; each genome chosen as a reference has been compared to the 124 other genomes. Because of the limitation and cannot be plotted onto a physical map of the reference strain, we chose to restrict the presentation to intra-clade pairwise comparisons.
Chromosomal arms are fast evolving regions In order to better characterize the decline of synteny along the chromosomal arms, the chromosome used as a reference was split in portions equivalent by the number of genes and OR and GOC values were plotted against the phylogenetic distance through as many pairwise comparisons as possible in our dataset (124). Different tests, by dividing the genome from 6 to 100 pieces of equivalent size, were carried out in order to visualize the behaviours that could typify the different chromosomal regions regarding their evolutionary speed. Dividing the genome into 100 small fragments had the advantage of precision but increased the amount of data to be displayed. In fact, this attempt gave too many overlapping curves and finally illegible results (not shown).
Funding information This work was supported as part of the  Investissements d'Avenir programme  ( ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE ). J. N. L. was the recipent of a doctoral grant from the  French Ministry of Higher Education, Research and Innovation (MESRI)  awarded by the  doctoral school Structure and Dynamics of Living Systems  (ED SDSV n°577).
Introduction Until recently, and despite more than 14 disease modifying therapies (DMTs) approved for adult multiple sclerosis (MS), none were approved for children with MS. This has led to a substantial off-label use, often following adult guidelines. Traditionally, injectable DMTs such as β-interferons (IFN-β) and glatiramer acetate (GA) have been used and appear safe and effective in children  (Banwell et al. , 2006; Chitnis et al. , 2012; Ghezzi et al. , 2016; Hacohen et al. , 2020; Kornek et al. , 2003). Oral and intravenous DMTs, together referred to as newer DMTs, have been less frequently used and data on their efficacy and safety in paediatric patients are scarce  (Krysko et al. , 2018 (Krysko et al. , , 2020)). Several clinical safety and efficacy trials are ongoing(ClinicalTrials.
gov; ClinicalTrials. gov; Clin-icalTrials. gov) following the approval of fingolimod (FGL) for paediatric MS by the Food and Drug Administration and the European Medicines Agency in 2018  (Chitnis et al. , 2018). Understanding the clinical practice and attitudes towards treatment among clinicians treating children with MS is vital to guide future therapeutic trials and develop guidelines that reflect practice. We therefore performed an online survey within the International Paediatric Multiple Sclerosis Study Group (IPMSSG) with the aim of studying how treatment of paediatric MS is organized internationally, whether there is a consensus in treatment choices and if these have been affected by the COVID-19 pandemic.
To increase the response rate, a subsequent reminder was sent. The survey was administered in English only. Data were tabulated descriptively. For each result, only the respondents completing that specific question were included in the denominator. For relevant variables, categorical data were analysed with Fisher's exact test. P-values lower than 0. 05 were considered statistically significant. Analyses were performed using Stata, version 16 (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC).
Background section with demographic data Out of the 130 members of the IPMSSG who were invited to participate in the survey, 66 (51%) responded. Of these, 59 completed the entire survey. The respondents were distributed over 25 countries, where US was by far the most represented (supplementary Table  1 ). In this context, the term "paediatric" varies between different countries, with the upper age generally varying from 16 to 22 years (range 7-22). Almost all (65/66, 98%) of the respondents answered that children with MS were referred to their hospital from other hospitals. Most of the respondents (47/66, 71%) were paediatric neurologists while 20% (13/66) were adult neurologists. A majority (45/66, 68%) had at least a 10-year experience taking care of children with MS (range 2-41). Almost half (32/66, 48%) worked at a site with at least 30 children with MS (range 2-200) at the time of the survey.
1 ). Here, we contrast our initial intuition with objective data, by analyzing the list of beneficiaries of the COVID-fund allocated to the French fishing sector, in order to determine who benefited from it and whether this fund i) was allocated in line with the overarching objectives of the CFP and international, non-binding commitments such as those of the SDGs, or ii) instead reinforced existing inequalities and failed to secure the fulfillment of these objectives and commitments. In the following section (Section 2), we describe the data that was gathered and analyzed to answer the aforementioned questions. We then explore the most important trends that were identified in these data (Section 3), and endeavor to explain them (Section 4) and contrast them with international objectives and commitments (Section 5).
Materials and methods Our analysis of the allocation of the COVID fund to the French fisheries sector is based on two distinct datasets: i) the list of beneficiaries of the European Maritime and Fisheries Fund (EMFF), and ii) the European fleet register. All data that support the findings of this study as well as all codes (written in R language;  [25] ) used for figures and statistics are available through a Mendeley repository  [26]. An RMarkdown document provides a step-by-step explanation of the process used to assemble the data analyzed in this paper, as well as to produce the figures and statistics provided herein. Below, we succinctly explain the steps followed to clean, complete, and match these two datasets, so as to obtain the data that formed the basis of the present analyses (synthetized in Fig.
Dataset 1: EMFF beneficiaries The European Maritime and Fisheries Fund (EMFF) is the structural and investment fund that aimed to support the implementation and promotion of the objectives of the Common fisheries policy over the 2014-2020 period. As mandated by Regulation 508/2014 [27], all countries had to publish their list of EMFF beneficiaries every six months on a single, dedicated website/platform, including the names of these beneficiaries (only legal entities and natural persons) as well as Community fleet register (CFR) vessel identification numbers (when operations were linked to fishing vessels). We downloaded France's latest list of beneficiaries (6 December 2020 update) on its dedicated website Fig. 1. Diagram of the allocation of the COVID fund to the French fleet  [23]. The 'reference turnover' -which is not explained nor justified in the decree or elsewheredepends on the vessel length and the fishing area: 70,739 EUR and 138,075 EUR for vessels <10 m and between 10 and 12 m, respectively, when fishing in the Mediterranean Sea; 80,147 EUR and 261,127 EUR for vessels fishing elsewhere.
Fig. 2. Diagram summarizing the steps followed to clean, complete, and match the two primary (raw) datasets. These steps are briefly explained in this section, and thoroughly commented in the linked Mendeley repository  [26]. [28]. A series of modifications was then performed in order to simplify and prepare these data for our analysis (Fig. 1 , see scripts in linked repository for details;  [26] ). In particular, CFRs were harmonized so as to be matched with the EU fleet register (see next section). It is to be noted that out of the 3730 operations listed in this file and retained for the analysis, 802 corresponded to the COVID fund, out of which CFRs were not available for 222 (i. e. 27. 7% of the records; or 31. 9% of the total amount). In order to reduce this number of incomplete records and thus strengthen our analysis, a manual complementation of the CFRs was done, based on the names of the beneficiaries (also available in the linked repository for perusal, along with comments and sources where applicable).
Dataset 2: European fleet register We downloaded the European fleet register (referred throughout as 'the Register'; [29]) on 13 January 2021. It contains all historical records relating to fishing vessels flagged in each EU Member States: every time that a vessel's characteristics changed (e. g. calling port, tonnage, power, fishing gear etc. ), a new record was created and made available in the Register. We extracted the data pertaining to the French fleet. As per the EMFF data, a series of modifications was then performed in order to simplify and prepare these data for our analysis (Fig. 1 , see scripts in linked repository for details;  [26] ). In particular, we categorized: i) Fishing gears as broader categories (e. g. 'bottom trawls & dredges', 'pots & traps' etc. ) as well as either 'passive' (or 'static', i.
Combination of both datasets Once these two datasets ready, we matched them to one another so that EMFF-funded operations were linked, where applicable, to an actual vessel and its main characteristics (i. e. fishing gear, length, tonnage, and power). For the purpose of this research, we only focused on the COVID fund for this process, which was done incrementally: First, CFRs from the EMFF data were matched to those events from the Register categorized as 'Last event' and overlapping with the COVID fund (i. e. 'TRUE'; step 1). Then, for the remaining records, CFRs were matched to 'Older event' records still overlapping with the COVID fund (step 2); then to any records not overlapping with the COVID fund (step 3). At this stage, we were able to re-assign three correct CFRs thanks to beneficiary names.
This incremental process allowed us to ensure that CFRs from the EMFF data were matched to the best data available in the Register. As a final processing step, we endeavored to identify if any companies and groups (e. g. cooperatives) captured important subsidies. To do so, we extracted the list of projects that accounted for 30% of the overall COVID fund and assignedwhen possible and applicablethe concerned vessels to their parent companies. In case we identified other funded vessels (i. e. not in the top-30% list) owned by these companies, we also linked them together (see scripts in linked repository for details). Overall, France's list of EMFF beneficiaries amounted to 419. 8 million EUR, out of which 17. 1 million EUR were from the COVID fund (i. e. 4. 1%). In the present analysis, we focused on the 14.
Study design The Danish Diet, Cancer and Health Study is a prospective cohort investigation of nutrition, cancer, and other chronic diseases, described in detail previously  (13). The study was approved by the Danish Data Protection Agency (Ref no 2012-58-0004 I-Suite nr: 6357, VD-2018-117) and all participants provided informed consent. Requests to access the dataset may be sent to the Diet, Cancer and Health Steering Committee at the Danish Cancer Society. Briefly, between 1993 and 1997, 160,725 men and women, aged 50-64 y, who lived in the Copenhagen and Aarhus areas of Denmark, were invited to participate. Invitation was accepted by 57,053 people and the participants attended study centers for baseline assessment. Participants completed a self-administered, interviewer-checked questionnaire on sociodemographic factors and health behavior, while anthropometric measures and blood samples were obtained by professional staff.
Dietary intake was assessed by a 192item FFQ. Participants were then followed prospectively using Danish nationwide registries to identity and record follow-up information. Key registries included the Civil Registration System, which provides information on vital status and emigration; the Integrated Database for Labor Market Research, which contains annual income data; the Danish Registry of Causes of Death, which provides information on mortality; and the Danish National Patient Register (DNPR), which has recorded nearly all (∼99. 4%) hospital admissions in Denmark since 1978. The DNPR further includes diagnosis information, classified until 1993 according to the International Classification of Diseases (ICD), 8th Revision (ICD-8) and thereafter, according to the 10th Revision (ICD-10)  (14).
Assessment of diet and flavonoid intake Calculations of nutrient and flavonoid intake have been described previously  (7, 13, 15). Briefly, data on the flavonoid content of foods were obtained from Phenol-Explorer (without hydrolysis of glycosides or esters) and the effect of food processing was taken into consideration using retention factors  (16). Intakes of individual flavonoid compounds were estimated using the flavonoid content reported in Phenol-Explorer for each food and beverage item in the FFQ. Based on chemical structure, all individual flavonoid compounds were summed into subclasses including: flavonols, flavanol monomers, flavanol oligo + polymers (including theaflavins), flavanones, flavones, anthocyanins, isoflavones, dihydrochalcones, dihydroflavonols, and chalcones. Total flavonoid intake was calculated as the sum of all individual flavonoid compounds from all food sources.