dataesr/scientific-paragraphs-categorization huggingface dataset:
All patients presented exocrine pancreatic insufficiency and 12% had diabetes. Finally, 42% of CF patients were treated with the modulator lumacaftor/ivacaftor (lumacaftor is a corrector molecule that stabilizes the CFTR protein maturation, and ivacaftor is a potentiator of the channel function)  [23]. Sputum samples were collected approximately every 6 months, and clinical data (i. e. , spirometric measurements, presence of germs, exacerbations, and treatments) were recorded on the same day. To treat sputum samples and extract genomic DNA, we adapted and combined previously well-established methods to provide a streamlined strategy that could be routinely used for patient follow-up. Briefly, to minimize contamination with squamous cells from buccal mucosa, we selected mucus plugs from saliva and dispersed them with Sputolysin ®.
Biomarker Discovery in Nasal Epithelial Cell Samples from the MethylCF Cohort During a previous study, we generated a genome-wide DNA methylation dataset in nasal epithelial cell samples from CF patients and healthy controls (MethylCF cohort)  [13]. Herein, we used this methylation dataset to select potential biomarkers of lung disease severity in cystic fibrosis. We combined differential DNA methylation and correlation analyses and selected top CpG sites. CpG dinucleotides were considered differentially methylated when the difference between the median β value for mild patients and the median β value for severe patients (∆β) was >0. 1, with an associated p-value < 0. 01. The β value ranges from 0 (no methylation) to 1 (full methylation). Correlations between DNA methylation and FEV1% predicted were calculated through Spearman test adjusted for age and sex.
Biomarker Discovery in Nasal Epithelial Cell Samples from the MethylCF Cohort During a previous study, we generated a genome-wide DNA methylation dataset in nasal epithelial cell samples from CF patients and healthy controls (MethylCF cohort)  [13]. Herein, we used this methylation dataset to select potential biomarkers of lung disease severity in cystic fibrosis. We combined differential DNA methylation and correlation analyses and selected top CpG sites. CpG dinucleotides were considered differentially methylated when the difference between the median β value for mild patients and the median β value for severe patients (∆β) was >0. 1, with an associated p-value < 0. 01. The β value ranges from 0 (no methylation) to 1 (full methylation). Correlations between DNA methylation and FEV 1 % predicted were calculated through Spearman test adjusted for age and sex.
Biomarker Assessment in Sputum Samples from the MethylBiomark Cohort Seven CpG markers were re-assessed in sputum samples from the prospective longitudinal MethylBiomark cohort. DNA methylation was measured by pyrosequencing in 43 sputum samples collected at the baseline visit (V1). Next, we correlated DNA methylation of each CpG site with continuous or binary phenotypic traits of the cohort: lung disease severity, lung function metrics, and presence of chronic infections and treatments (Figure  2  and Table  S2 ). The clinical data were filed on the same day the sputum samples were collected (Table  2 ). Age and sex adjusted Spearman correlations between DNA methylation and FEV1%. † β (β) values from 450 K array, where 1 corresponds to full methylation and 0 to no methylation. ∆β = median β mild CF patients-median β severe CF patients.
Biomarker Assessment in Sputum Samples from the MethylBiomark Cohort Seven CpG markers were re-assessed in sputum samples from the prospective longitudinal MethylBiomark cohort. DNA methylation was measured by pyrosequencing in 43 sputum samples collected at the baseline visit (V1). Next, we correlated DNA methylation of each CpG site with continuous or binary phenotypic traits of the cohort: lung disease severity, lung function metrics, and presence of chronic infections and treatments (Figure  2  and Table  S2 ). The clinical data were filed on the same day the sputum samples were collected (Table  2 ). Of interest, DNA methylation of ATP11A (cg11702988) positively correlated with lung function (FEV 1 and FVC) and BMI, and negatively correlated with lung disease severity (mild, intermediate, and severe CF patients) and with the presence of chronic P.
The cg11702988 (ATP11A) Maps to a Predicted Enhancer in the Lung The cg11702988 (chr13:113,422,177 Hg19) maps to intron 1 of ATP11A. This gene is ubiquitously expressed in human tissues, with a maximum in alveolar cells type 1 and 2 in the lung, according to transcriptomic datasets in human tissues and single cells (the Genotype-Tissue Expression (GTEx) Project and Human Protein Atlas, respectively) (Figure  4a )  [24, 25]. To assess the chromatin structure around this CpG site, we retrieved ChIP-seq datasets from the Roadmap Epigenomics Project and analyzed three histone modifications that are predictive of active cis-regulatory sequences  [22]. In adult lungs, the cg11702988 was associated with high levels of H3K27 acetylation and H3K4 mono-methylation, in the absence of H3K4 tri-methylation (Figure  4b ).
Discussion Robust biomarkers are required for CF monitoring, to guide treatments, stratify patients, and as outcome measures in clinical trials. In the present study, we outline a strategy to identify DNA methylation markers in sputum samples from CF patients. DNA methylation is a very robust epigenetic mark and can be easily and quantitatively assessed from few hundred nanograms of DNA. Thus, DNA methylation biomarkers have great potential in terms of disease diagnosis or prognosis and prediction of drug response  [12]. In this pilot study, we preselected seven potential biomarkers in the MethylCF cohort using an array hybridization DNA methylation dataset and successfully replicated a biomarker in the MethylBiomark cohort. To the best of our knowledge, we carried out the first longitudinal analysis of DNA methylation in CF patients.
Furthermore, although no methylation quantitative trait locus was associated with this dinucleotide in the meQTLdb and Pancan_meQTL databases  [35, 36] , we still cannot rule out the possibility that an unknown cisor transsequence variant affects DNA methylation at this CpG site. This study has limitations. Even though we identified a new epigenetic biomarker of lung disease severity in CF, additional validation studies in larger cohorts are required to translate the biomarker to the clinical use. Herein, we selected markers in nasal epithelial cell samples and replicated them in sputum samples. Likely, more markers could be successfully identified, if discovery and replication were carried out in the same tissue type.
Seven biomarkers were selected using a DNA methylation dataset generated in nasal epithelial cell samples from the MethylCF cohort. Age and sex adjusted Spearman correlations between DNA methylation and FEV 1 %. † β (β) values from 450 K array, where 1 corresponds to full methylation and 0 to no methylation. ∆β = median β mild CF patients-median β severe CF patients.
Methodological Note on the Data on Naval Deployments The source used to gather data on French naval deployments in the Indian and Pacific Oceans (Figure  1  and Table  1 ) is the French Navy magazine Cols Bleus. Data were gathered for the 2012-2019 (both included) period. Prior to 2012, data were not included because the magazine listed missions differently (e. g. focus on some major missions, exclusion of smaller and middle-sized missions) which would have prevented comparability over time. The data were organized along the following categories:  Mission Statement. The name of the mission as used in a given issue of the magazine is indicated. Each issue of Cols Bleus lists so-called "generic" and "non-generic" missions. Generic missions take place on a regular basis and always under the same mission statement, such as "Maritime Surveillance" or "Fisheries Policing.
2 ; checked on all plants). LER was therefore averaged over the last 4 h of each period. In wheat, the LER of a given leaf is not steady and depends on leaf rank. Therefore, in order to compare leaves of different lengths (relative to their final length) and rank, the LER at the target temperature was normalized by LER at the reference temperature (20 °C) measured during the same night. To determine the cardinal temperatures of normalized LER, the data were fitted with a 3-parameter segmented linear-function equation constrained to be equal to 1 at the reference temperature: f (T) =    (T-Tmin) (Tref-Tmin) , T < T opt (T-Tmax)(Topt-Tmin) (Tref-Tmin)(Topt-Tmax) , T ≥ T opt (1) where T is the temperature of the leaf meristematic zone, T ref is the reference temperature at which f(T) is equal to 1, T min is the base temperature at which the normalized LER equals zero, T opt is the temperature at which the normalized LER is maximal, and T max is the supra-optimal temperature at which the normalized LER equals zero (all temperatures in °C; see Table  1  for a full list of definitions of traits).
Principal component analyses (PCAs) were performed using the function PCA of the FactorMineR R package  (Lê et al. , 2008). We considered several sets of traits (e. g. all traits, or traits linked to sensitivities to temperature and VPD only), with data at the accession level. Values of each species on the first five axes of the PCA were determined by calculating the barycentre of all accessions in each species. A hierarchical clustering on principal components (HCPC) analysis of species was then performed based on these first five axes of the PCA using the HCPC function of the FactorMineR package. In the case of the analysis of the set of traits comprising only the five traits linked to sensitivities, and because the phenotypic space was then automatically reduced to five dimensions (five variables), the hierarchical clustering analysis was carried out based on these five traits rather than on principal components of the PCA.
Acknowledgements SL was supported by a  Convention Industrielle de Formation par la Recherche  (CIFRE convention no. 2017/1069 ) between  ANRT  and  ITK. The authors thank  François Balfourier  ( UMR GDEC, INRAE, Clermont-Ferrand, France ) and  Pierre Roumet  ( UMR AGAP, INRAE, Montpellier, France ) for advice on selecting the material and for providing seeds, and  Dr Jacques Le Gouis  and  Ms S. Rougeol  ( UMR GDEC, INRAE, Clermont-Ferrand, France ) for genotyping the major genes for the accessions used in this study.
The MitoCarta repository-the largest public database annotating mitochondrial proteins  (Calvo et al. , 2016) -FIGURE  6  PFA/G fixation coupled to antigen retrieval procedures are more specific towards the OMM pool of AURKA. (a-c) Maximal projections of representative 2D dSTORM micrographs from MCF7 co-stained for AURKA and TOMM20 (a), or for AURKA and PMPCB (b). The anti-TOMM20 and anti-PMPCB primary antibodies were detected using secondary antibodies conjugated to Alexa 647 and pseudocolored magenta, while the anti-AURKA primary antibody was detected using a secondary antibody conjugated to Alexa 555 and pseudocolored cyan. was recently found to contain only half of the mitochondrial proteome  (Antonicka et al. , 2020). Bio-ID-based proteomics techniques significantly expanded our view of the mitochondrial proteome  (Antonicka et al.
Image acquisition and reconstruction procedures Two-color dSTORM acquisitions were performed on an Abbelight super resolution microscope (Abbelight, France) constituted by an Olympus IX83 microscope, an Abbelight SAFe 180 nanoscopy module, two Oxxius lasers at 640 and at 532 nm (Oxxius, France), a pco. panda sCMOS camera (PCO AG, Germany), equipped with a 100X oil-immersion objective (NA 1. 5), and driven by the NEO software (Abbelight, France). Particle quantification and the reconstruction of the super resolution images was performed with the NEO software, using the Gaussian fitting algorithm integrated within. To evaluate the effect of the fixation method on mitochondrial integrity, TOMM20 or PMPCB-specific images were acquired with a home-made dSTORM microscope, composed of a Nikon TIRF system, a 640 nm laser (Toptica, Germany), an EMCCD camera (Andor, UK) and controlled by the NIS software.
A 100X objective (NA 1. 49) and a 1. 5X lens were used to acquire images. Images were reconstructed using the UNLOC plugin for the ImageJ software (ImageJ, NHI),  (Mailfert et al. , 2018). 30000 frames of approximately 250×250 pixels were analyzed with the parameter-free UNLOC [UNsupervised particule LOCalization algorithm v1. 0] algorithm in the high-density mode, allowing to fit with a multiple emitter fitting model, to correct the local and temporal variations of the background. The xy drift was corrected using a robust correlation method, and data were filtered according to these parameters (No filters parameters were modified except SNR Min = 20). Finally, an Integrated-Gaussian mode was applied to render the reconstructed images with a sub-pixel zoom factor of 8, leading to a final pixel size of 107 nm.
The authors thank S. Dutertre at the Microscopy Rennes Imaging Center (MRic, Biologie, Santé, Innovation Technologique -BIOSIT, Rennes, France) for assistance, M. Garfa and all the colleagues at the Cell Imaging Platform, Structure Fédérative de Recherche Necker (Paris, France) for their support. MRic is member of the national infrastructure France-BioImaging supported by the French National Research Agency (ANR-10-INBS-04). This work was supported by the Centre National de la Recherche Scientifique (CNRS), an intramural BIOSIT grant, grants from the Ligue Contre le Cancer Comités d'Ille et Vilaine, des Côtes d' Armor et du Finistère, the Fondation ARC pour la Recherche sur le Cancer, and an intramural technology-transfer grant from the Réseau Technologique Microscopie photonique de Fluorescence Multidimensionnelle (RTmfm) to G.
By mid-July 2020, among 2654 registered clinical trials from 43 countries aimed at testing the capacity of compound at preventing severe COVID-19, 239 included HCQ treatment or prophylaxis while 82 addressed chloroquine efficiency (Clinicaltrials. gov). Still today, the debate remains fierce between those who consider HCQ to save lives, those who find no significant impact on COVID-19 progression and those who still claim that HCQ is toxic. 29e34  This does not make it possible to conclude whether HCQ is beneficial or not, it simply indicates that the case series and treatment protocols are different and that meta-analysis algorithms are either unsuitable or misused. As knowledge about the disease grew it became evident that one of the major issues to be addressed in severe COVID-19 was that of thrombosis.
Funding This work was supported by the  French Government  under the  « Investissements d'avenir » (Investments for the Future) program  managed by the  Agence Nationale de la Recherche. (French ANR: National Agency for Research)  (reference: Mediterranee Infection  10-IAHU-03 ), the  Region Provence Alpes Cote d'Azur and European funding FEDER PRIMI.
By leveraging Tara Oceans data from all major oceanic provinces, including the Arctic Ocean, we inferred a comprehensive global ocean cross-domain plankton co-occurrence network from sequencing data. We built statistical niche models to predict realized niches of planktonic taxa, across kingdoms, and from pole to pole. These were then mapped onto the network and used to evaluate both local and global robustness of plankton community structures to simulated environmental changes. In addition, we integrated climate model projections [CMIP6 model  (33)  scenario SSP2-4. 5] for predicting affected proportions of plankton taxonomic groups, for which we considered environmental ranges corresponding to global mean anomalies projected for the end of the century. Noticeable efforts have used the ecological niche concept to identify open ocean physical conditions governing phytoplankton biogeography  (34)  and also to better formalize central biogeochemical processes through the definition of key plankton functional types  (35).
While planktonic interactions remain challenging to validate, our predictions are useful to further our understanding of ecosystem functioning and may constitute useful guidance for coculture experiments and a database for hypothesis testing. Today, high-throughput coculture experiments using microfluidics  (84)  and fabricated synthetic microbial ecosystems may help fill this gap  (85). Climate scenarios predict global changes in temperature, pH, and nutrient concentrations, which all greatly influence plankton physiology. Temperature can directly affect bacterial growth  (86) , grazing rates  (87) , and phytoplankton metabolism  (88). Nitrogen availability is a primary limiting factor for marine phytoplankton  (89). Ocean acidification caused by rising atmospheric CO 2 can affect phytoplankton growth rates and is predicted to have a greater impact than warming or reduced nutrient supply on plankton ecological functions  (90).
Data description From 2009 to 2013, the Tara Oceans expedition collected samples at more than 200 stations across all significant oceanic provinces from oligotrophic to polar regions. Sampling stations were selected to represent distinct marine ecosystems at global scale, for which the sampling strategy and the methodology have been previously described  (96). Sample provenance is described in table  S1. Environmental data measured or inferred at the depth of sampling are available in table  S2  and published at PANGAEA, Data Publisher for Earth and Environmental Science (www. pangaea. de). In this study, we limited our analyses to the euphotic zone, including only the samples from surface (SRF) and the Deep Chlorophyll Maximum (DCM). Two prokaryote-enriched size fractions (0. 2 to 1. 6 m and 0.
2 to 3 m) were available and included in the analyses. For eukaryotes, the following size fractions were included (and consolidated as described below) in the analyses: "0. 8 to 5 m and 0. 8 to 2000 m," "3 to 20 m and 5 to 20 m," "20 to 180 m," and "180 to 2000 m. " Because of these sampling constraints and the nonsystematic sequencing of all available samples, the Tara Oceans dataset is heterogeneous. Specifically, at polar stations, fractions 0. 8 to 5 m and 5 to 20 m are less represented. Conversely, in nonpolar stations, sequencing data for the fraction 3 to 20 m are nearly absent. To overcome this issue and increase sampling coverage, we considered that fractions 3 to 20 m (for Arctic samples) and 5 to 20 m (for non-Arctic samples) were equivalent, as well as fractions 0. 8 to 5 m and 0.
Data processing and taxonomic annotations For the prokaryote-enriched size fraction (0. 2 to 1. 6 m and 0. 2 to 3 m), taxonomic profiling was performed using 16S ribosomal gene fragments directly identified in Illumina-sequenced metagenomes (4). To profile taxonomic abundances from metagenomes using a reference-based method, two goals need to be achieved: (i) The reference database needs to be a balanced representation of the diversity space, and thus, the under-/overrepresentation of some taxa in the database needs to be corrected, which can be done by defining OTUs at a higher level (e. g. , at genus level); (ii) the taxonomical units to be detected (if not defined in the database) need to be defined, which is the case in the SILVA database that is cataloguing reference sequences at the species and strain levels.
Thus, we performed a preclustering of the SILVA database. By preclustering, OTUs at 97% similarity, OTUs are defined above the genus level, which also serves to balance the unequal representation of different taxa in the database. The 97% identity cutoff for the full 16S ribosomal RNA (rRNA) sequence was chosen as it matches the classical definition of 16S OTUs. Then, extracted 16S reads, named miTags, were mapped to cluster centroids of taxonomically annotated 16S rRNA gene reference sequences from the SILVA database (59) (release 128: SSU Ref NR 99), which had been clustered at 97% sequence identity beforehand using USEARCH v9. 2. 64. Additional methodological details are available in (4), and we used the OTU-level abundance matrix as provided by the authors. For the eukaryotic taxonomic   15  profiling, we used metabarcoding data with the same methodology as in  (58)  to define OTUs.
18S rRNA gene V9 amplicons were clustered with the Swarm version 2. 1. 1 (with fastidious option, and d = 1) (97) and taxonomically and functionally annotated by global pairwise alignment (vsearch -usearch_global command) against an updated version (available at http://doi. org/10. 5281/zenodo. 3768951) of the PR2_V9 reference database (3). 18S rRNA gene V9 region polymerase chain reaction (PCR) primers also amplify some 16S rRNA gene V9; thus, we decided to filter out OTUs not assigned to eukaryotic reference sequences with more than 80% of identity ( "Bacteria": 35,448 OTUs, "unassigned": 31,406 OTUs, "Archaea": 1806 OTUs, "root": 58 OTUs, and "Organelle": 583 OTUs). For the prokaryotic abundance matrix, we filtered out miTags assigned to "Eukaryota" (5283 OTUs), Chloroplast (468 OTUs), and Mitochondria (74 OTUs).
After this filtering, we worked with six distinct matrices corresponding to each size fraction considered (see the Supplementary Materials). S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 10 of On the basis of these taxonomic affiliations, we classified all taxa into MPGs as in  (3). For prokaryotes, photosynthetic bacteria (i. e. , cyanobacteria) were distinguished from heterotrophic/chemotrophic bacteria and archaea. For protists, the functional annotations of PR2_V9 (http://doi. org/10. 5281/zenodo. 3768951) were used. It encompasses a wide variety of protist taxa that are assigned to major functional groups: photosynthetic/mixotrophic protists, endophotosymbionts, hosts with endophotosymbionts (photohosts), parasitic protists, and free-living heterotrophs or phagotrophs (heterotrophic protists).
Network inference and stability procedure The network inference was performed using FW v0. 13. 1 with default parameters  (38). FW relies on the local-to-global learning framework and infers direct associations by searching for conditional dependencies between OTUs. Several heuristics are then applied to connect these "local" dependencies and infer a network. FW is significantly faster than other methods while achieving better or similar results and gives the possibility to include metavariables (such as the temperature). Although the latter feature seemed appealing, very few OTU-environmental factor associations were detected, which advocates for developing a complementary approach to study the environmental influence (see the section, "Network-based robustness analyses"). While FW includes a heterogeneous mode (FlashWeave-HE) and the Tara dataset is heterogenous itself, the low number of samples prevented its use.
Estimation of FDR Three null models were generated using two R packages (picante v1. 7 and HMP v1. 6). The HMP library provides the Dirichlet. multinomial function, which allows data matrix generation of OTUs following a Dirichlet distribution. Picante comes with a random-izeMatrix function and several methods to randomize the matrix. We used the frequency (that maintains OTU occurrence frequency) and trialswap (maintaining OTU occurrence frequency and sample OTU richness) approaches. Then, networks were inferred from these matrices using FW and the same procedure as for the observed matrices. We then estimated an FDR by comparing common edges between the observed and simulated networks. The highest FDR we obtained was 3. 6% (with a number of iterations set to 10 8 ) using the trialswap method.