dataesr/scientific-paragraphs-categorization huggingface dataset:
Hospitalization With COVID-19 Information on all hospital stays is integrated into the SNDS (usually in July of the following year). In April 2020, the French government encouraged hospitals to report COVID hospital stays according to an exceptional, fast-track modality (once a week or once a fortnight; called fast-track PMSI). Our study was based on hospital discharge data available on June 7, 2020. At this date, 86 623 patients hospitalized with COVID-19 were reported and identified, including 83 445 (96%) for whom hospital discharge data could be linked to reimbursement data (ie, with no error concerning the anonymous identifier). Of these 83 445 patients, 14 566 died in.
Although this database does not guarantee that the subject actually took the antihypertensive drug dispensed, selecting patients to whom the drug was dispensed at least 3× should reduce this drawback. The mean number of dispensations was 6 over a 12-month period and the mean number of months covered by at least one reimbursement in each cohort was 11 which suggests that the majority of the patients probably presented a good adherence to treatment. Finally, little information is available on lifestyle, and variables, such as obesity or smoking, are underestimated. Santé publique France, the French center comparable to US Centers for Disease Control and Prevention, estimated that between March 1, 2020 and June 9, 2020, 102 863 patients were hospitalized with COVID-19 in France, including 18 912 patients who died in hospitals.
However, the OSIS system was not designed or suited to store and serve final data for the long-term. In the final step the data were thus published and made freely available at the World Data Center PANGAEA  4  or at other more specific data centers. Because of the diverse scientific fields and measurements involved, the rules of the data policy were quite generic. At the same time, an important goal was to ensure the timely exchange of data between the various research groups within the SFB 754. Within 3 months after data collection, meta-data for the measurements had to be entered into OSIS, and after 6 months initial versions had to be uploaded. The final publication of the data on PANGAEA was due 3 years after collection leaving sufficient time for analyses by members of the SFB 754. To support the adherence to the rules automatic reminders were sent by OSIS to the scientists responsible for the data sets.
More than 1100 data sets have to date been published on PANGAEA  5 (see text footnote 5 for a complete and up-to-date listing), while a small number (about 100) is still being processed and will be published in the near future. For easier accessibility, the data from the different scientific fields using different methods and instruments have been separated into data collections at PANGAEA (see Table  2 ). Some of the data sets have been published elsewhere on more specialized databases. These are explicitly mentioned in the text below.
OBSERVATIONAL AND EXPERIMENTAL METHODS AND DATA During its 12-year existence, the SFB 754 used a large number of observational and experimental methods to assess the physical and biogeochemical state of the tropical oceans and the interactions between its components. The resulting data sets have been described, assessed, used, and incorporated in a large number of publications. Here we briefly summarize the methods used and refer to the relevant publications in which the methods have been described in detail. Data set collections have been created on PANGAEA for each of the following subsections (see Table  2 ). Each collection on PANGAEA also includes a pdf document with a summarizing table listing ancillary information and all relevant data set DOIs. Table  1  lists the research cruises with their start and end dates together with the DOIs of the cruise reports where additional information about the data collected and methods used can be found.
Salinometry The conductivity sensors of the CTD were calibrated against International Association for the Physical Sciences of the Ocean (IAPSO) Standard Seawater samples with known conductivities using Guildline Autosal B instruments. On all cruises two Autosals were available and used to measure between 100 and 1000 samples (typically 300-400 for a cruise or 4-5 per CTD cast). The procedures used for the calibration followed the recommendations in the GO-SHIP manual  (Kawano, 2010). The results from the salinometer measurements are included in the source files for CTD data published on PANGAEA (Krahmann and Mehrtens, 2021c).
Ocean Turbulence Measurements Ocean turbulence measurement programs were carried out during 22 cruises to quantify the dissipation rate of turbulent kinetic energy and infer rates of turbulent mixing (Dengler and Mehrtens, 2021; see Table  2  and Supplementary Table 5 ). The shipboard microstructure profiling systems (MSS) were manufactured by Sea & Sun Technology and consisted of a profiler (MSS90-D, S/N 26, 32, and 73), a winch having 500-1000 m of cable, and a data interface. All profilers were equipped with three microstructure shear sensors, a fast-response temperature sensor (PF07), an acceleration sensor, and two tilt sensors as well as conductivity (Sea & Sun Tech. ), temperature (Sea & Sun Tech. ), pressure (Keller), turbidity (Seapoint), and oxygen sensors sampling with a lower response time.
, 2019;  to date there is no accepted standard for seawater measurements). The final qualitycontrolled data are available through the Surface Ocean CO 2 Atlas (SOCAT 8  ) and the MarinE MethanE and NiTrous Oxide (MEMENTO) 9  database as well as on PANGAEA (Arévalo-Martínez and Mehrtens, 2021; see Table  2  and Supplementary Table  13 ).
Physical DNA libraries are archived at GEOMAR and the University of Southern Denmark, and subsamples are available on request from C. Löscher. In addition to this mainly pelagic work, transcriptomes, and genomes of the denitrifying benthic foraminifera Globobulimina turgida and G. auriculata from the seasonally hypoxic Swedish Gullmar Fjord were analyzed  (Woehle et al. , 2018). The obtained information was used to describe the foraminifera unique eukaryotic ability to denitrify and colonize low-oxygen environments. Sequences were submitted to the NCBI's Sequence Read Archive (accession numbers SRR6202052 -SRR6202078) and to the transcriptome sequencing archive (accession numbers GGCE00000000 and GGCD00000000). The genome assembly was submitted to NCBI (draft genomes PIVH00000000-PIWH00000000; unassigned contigs: PJEL00000000).
Scanned image data are available on EcoTaxa 11   (Picheral et al. , 2017)  upon request from R. Kiko and H. Hauss. Taxon-specific biomass and metabolic rate estimates are publicly available on PANGAEA  (Kiko and Hauss, 2019; Kiko et al. , 2020). To expand the ecological knowledge on fragile organisms (such as giant rhizaria, medusae, ctenophores, and siphonophores) in situ imaging techniques were employed in addition to net sampling. An Underwater Vision Profiler 5 (UVP5;  Picheral et al. , 2010)  was routinely mounted on the CTD/rosette during most SFB 754 cruises since 2012  (Kiko et al. , 2021a;  see Table  2  and Supplementary Table 21 ). During the cruises in 2012 and 2013 a UVP5 was used that was kindly provided by the Laboratoire d'Océangraphie de Villefranche-sur-Mer (France). The instrument consists of one down facing HD camera in a steel pressure case and two red LED lights which illuminate a 0.
ACKNOWLEDGMENTS We would like to extend our sincere thanks to the authorities from the countries bordering the  Eastern Tropical Pacific  and the  Tropical Atlantic  for granting the permissions to conduct studies in their territorial waters. The captains and crews of the research vessels made our measurements possible with their much appreciated invariably skilled support. Principal investigators, scientists, students, technicians, administrators, and support personnel all worked together to form the SFB 754 and a big thank you goes to all of them. The  Deutsche Forschungsgemeinschaft  of course deserves our deep gratitude for making such long-term and large-scale science projects possible.
FUNDING We gratefully acknowledge the financial support for the " Sonderforschungsbereich 754: Climate-Biogeochemistry Interactions in the Tropical Ocean " by the  DFG. The main support for the cruises M91 and MSM18/3 came through the  BMBF-funded joint projects SOPRAN I-III  ( FKZ 03F0462 ,  03F0611 , and  03F0662 ). Cruise SO243 was mainly supported by the  BMBF -funded project  SO243 -ASTRA-OMZ (FKZ 03G0243A ). Support for cruises  ATA_IFMGEOMAR_4 ,  M80/1 ,  MSM18/2 ,  MSM22 ,  M130 , and M145 came also through the  BMBF-funded joint projects NORDATLANTIK, RACE , and  RACE II  ( FKZ 03F0443B ,  03F0651B , and  03F0729C ). Cruises  MSM22  and  M105  received further support from the  EU -funded project  CARBOCHANGE  (grant  264879 ).
We compared all our results with a matched published dataset of H3K27me3  (Zylicz et al, 2019). Importantly, we validated the specificity of H4K20me1 antibody using a peptide array  (Fig EV5A)  and controlled for Xist RNA induction efficiency  (Fig EV5B). Upon sequencing, reads were split according to content of allele-specific single nucleotide polymorphisms (SNPs: B6 mapping to Xi; Cast mapping to Xa). Such allelic information was analysed, revealing progressive enrichment of B6-specific reads (originating from Xi) upon Xist induction for H4K20me1 as is the case for H3K27me3  (Fig EV5C). We analysed relative B6-read enrichment within 10-kb windows across the whole X chromosome normalised to t = 0 h (Fig  4B ). This revealed H4K20me1 accumulation after 8 h of DOX induction, a time point when H3K27me3 also starts to accrue but significantly later than initial H2AK119Ub enrichment  (Zylicz et al, 2019).
For immunofluorescence, HeLa cells were plated in a 24-well glass bottom plate (IWAKI) and transfected with expression vectors for HaloTag-tagged KDM6B and KDM4D. Twenty-four hours after transfection, the cells were fixed, permeabilised and blocked as described above. The cells were incubated for 1 h at room temperature with 5 μg/ml H3K27me3-mintbody, 2 μg/ml Alexa Fluor 488conjugated anti-H3K27me3 (CMA323/1E7), or 2 μg/ml Alexa Fluor 488-conjugated anti-H3K9me3 (CMA318/2F3) in 10% Blocking One-P (Nacalai Tesque) containing 1 μg/ml Hoechst33342 and Janelia Fluor 646 HaloTag Ligand (a gift from Luke Lavis). After washing three times with PBS, fluorescence images were sequentially collected using Nikon Ti-E equipped with a 40 × PlanApo (NA 0. 9) lens. The nuclear intensities of fluorescence signals were quantified using NIS-elements analysis software.
sgRNA-dCas system to label X-linked loci To search the target microsatellite repeats for dCas9-GFP-mediated genome visualisation, we first enumerated the sequences of N 20 GG and N 21 GG whose specificities in a chromosome are greater than 19. The specificity of a sequence s in chromosome c is defined to be the ratio f c ðsÞ=1 þ ∑ i≠c f i ðsÞ, where f c (s) denotes the frequency of s in c. Since this entails that sequences appearing less than 20 times in all chromosomes are irrelevant, we employed the k-mer counting software DSK (version 2. 1. 0; (Rizk et al, 2013) ) to obtain a list of 22-and 23-mers with a copy number > 19 on a chromosome. These sequences and their complementary sequences were used as inputs to the alignment software Bowtie (version 1. 1. 2; (Langmead et al, 2009) ) against the FASTA files (GRCm38, Ensembl 84).
03 --alignIn-tronMax 1 --alignEndsType EndToEnd --outSAMattributes NH HI NM MD]  (Dobin et al, 2013). Duplicates were discarded with Picard MarkDuplicates (1. 65) with options [REMOVE_DUPLICATES=true] (https://broadinstitute. github. io/picard/) and reads mapped on blacklisted regions from Encode Consortium were discarded. SNPSplit (0. 3. 2)  (Krueger & Andrews, 2016)  was then used to generate allele-specific BAM files by separating the alignment into two distinct alleles (CAST and B6) based on SNPs information downloaded from Sanger. Bigwig files were created with bedtools genome CoverageBed (2. 27. 1)  (Quinlan & Hall, 2010) , using a scale factor calculated on the total library (10,000,000/total reads) for both allele-specific bigwigs, and loaded on UCSC genome browser. H3K27me3 nChIP-seq data were downloaded from GSE116990 and processed identically to H4K20me1 nChIP-seq.
Counts and normalisation For all defined windows, total and allelic reads overlapping those features were then counted using featureCounts (1. 5. 1), with options [-C -p -P]  (Liao et al, 2014). Then, analysis was done based on normalised reads from B6 allele (allele of the inactive X chromosome). For each sample, a normalisation factor was calculated with the trimmed mean of M-values method (TMM) from edgeR package  (Robinson et al, 2010) , based on B6 reads overlapping consensus peaks located on autosomes. Peaks of each sample were first identified using MACS2 (2. 0. 10) with -B --broad options. For each replicate, all peaks coordinates were merged using bedtools merge (2. 27. 1)  (Quinlan & Hall, 2010). Then, common regions between merged peaks coordinates of each replicate were selected using bedtools intersectBed (2.
Acknowledgements We are grateful to members of the  Heard Lab  ( Curie Institute and EMBL Heidelberg ), especially  Francois Dossin , and members of the  Kimura Lab  ( Tokyo Tech ) for their help and critical input to this project. We also want to thank the  Bioimaging facility at iMM JLA  for their technical support with fluorescent light microscopy and imaging analysis, and the  Biomaterials Analysis Division, Open Facility Center ,  Tokyo Institute of Technology  for DNA sequencing analysis. We thank  Yolanda Markaki  and  Heinrich Leonhardt  ( LMU Munich ) for sgRNA-dCas9 plasmid constructs and  Luke Lavis  for JF646 dye. We thank  Claus S. Sørensen  for sharing H4K20me2 antibodies. This work was supported by  Fundação para a Ciência e Tecnologia (S. T. d. R) , project grants  PTDC/BIA-MOL/ 29320/2017  IC&DT (A.
Data availability The accession number for the sequencing datasets reported in this paper is GEO: GSE153146 (http://www. ncbi. nlm. nih. gov/geo/que ry/acc. cgi?acc=GSE153146). Nucleotide sequence data of 2E12LI scFv are available in several public databases (DDBJ/EMBL/ GenBank) under the accession number LC597262. Expanded View for this article is available online.
FFigure 4. 4 Figure 4. Native ChIP-seq reveals H4K20me1 and H3K27me3 co-accumulation during XCI. A Schematic representation of the experimental design. The hybrid TX1072 mouse female ESC line was used, in which Xist can be induced from the endogenous B6 allele. Time course nChiP-seq for H4K20me1 was performed and compared to a published H3K27me3 dataset (GSE116480) (Zylicz et al, 2019). B Quantification of average H4K20me1 (blue) and H3K27me3 (red) enrichment at the Xi (B6 allele) compared with t = 0 h in 10-kb windows spanning the whole chromosome. Shading is the interquartile range. C As in (B) but shown is normalised B6 enrichment to the average accumulation at 24 h. D Pairwise comparison of H4K20me1 and H3K27me3 accumulation dynamics (ED50) at the X chromosome. All 10-kb windows with ED50 < 24 h are plotted.
However, a recent study with simultaneous observations of IO, O 3 and sea-surface iodide in/over the Indian Ocean and the Southern Ocean found that the observed IO concentrations could not be adequately computed using inorganic iodine fluxes and the currently understood chemistry  [15]. Calculated sea-air fluxes of HOI and I 2 , using the MacDonald et al. [17]  global iodide fields and measured O 3 concentrations, were used as inputs to two independent global atmospheric chemistry models (GEOS-Chem and CAM-Chem). Both models suggested higher than observed IO levels in the Indian Ocean region but under-predicted [IO] for the Southern Ocean region  [15]. However, although there was also no correlation between measured and modelled IO levels across the entire dataset, the GEOS-Chem modelled IO showed a significant positive correlation with observed IO above the 99% confidence limit for data north of the polar front.
The new data include a large number of new observations from the previously very under-sampled Indian Ocean basin  [54] , so large-scale sea-surface iodide transects are now available for all ocean basins except the Arctic  [50]. Using the expanded dataset and a machine learning random forest regression (RFR) approach, we have Table  1. Summary statistics for observed and predicted global sea-surface iodide fields. Predicted values are annually averaged. Model simulations are for the present day. Note differences in maximum predicted values may arise from differences in the way very high observational data points have been treated when developing models. [Iodide], nM mean standard deviation lower quartile median upper quartile maximum observational datasets    2a , the predicted iodide distributions from the previous statistical relationships  [17, 49]  and from Sherwen et al.
[51]  all reflect the large-scale observed ocean distribution of iodide, with high concentrations in low latitude warm waters, and low iodide concentrations at high latitudes in seasonally overturning cold waters  [49, 50]. The iodide concentrations calculated in Sherwen et al. [51] , using the RFR approach, better capture the observed spatial variability and produce significantly higher concentrations (40% on a global basis) than the commonly used MacDonald et al. [17]  parametrization (figure  2b ). However, while these statistical relationships provide a generally good fit to the observational dataset, their extrapolation beyond the data range for which the relationships are derived cannot be carried out with confidence, nor do they allow prediction of whether oceanic surface iodide may change in the future, or how it may have changed in the past.
multivariate machine learning parametrization. MacDonald et al. [17] 5 9 3 5 1 7 5 1 8 7 1 2 6 Chance et al. [49] 1 2 8 6 5 4 9 1 2 2 1 7 9 2 2 6 Sherwen et al. [51] 106 46 52 106 139 220 Wadley et al. [52] 122 75 65 100 149 973 [51]. global biogeochemical model simulation. generated a high-resolution (0. 125°× 0. 125°, ∼12. 5 km × 12. 5 km) monthly dataset of present-day global sea-surface iodide[51]. The iodide observations were used as the dependent variable and co-located ancillary parameters (including temperature, mixed layer depth, salinity and nitrate) from global climatologies as the independent variables. As shown in figure.
Insect-microorganism interactions have been widely studied within apocrites (bees, wasps, ants), but few studies are related to termite-microorganism interactions outside the trophobiosis  [5] [6] [7] [8]. However, examples of antimicrobial compounds produced by microorganisms associated with termites from French Guiana were previously published in the literature, especially by the research group of D. Stien and V. Eparvier  [9] [10] [11] [12]. In the present work, the termite-associated microorganism strain collection at the Institute of Chemistry of Natural Substances (ICSN) was reinvestigated using newly developed metabolomic tools. Indeed, time-consuming purifications and rediscovery of active known compounds are recurring problems in natural product research. Dereplication using mass spectrometrybased molecular networking approaches can be implemented to overcome these drawbacks, allowing for the targeting of specific metabolites through the tandem mass spectrometry dataset organization that they confer  [13] [14] [15] [16] [17] [18].
Penicillium Sclerotiorum Selection The 109 crude extracts of microorganisms associated with termites were first analyzed by liquid chromatography coupled to tandem high-resolution mass spectrometry (LC-HRMS/MS) in data-dependent analysis (DDA) mode and in positive ion mode. Extracts were also biologically tested against two human pathogens, i. e. , Candida albicans and Trichophyton rubrum (Figure  S1 ). The MS/MS data generated were processed to create a molecular network (MN) by MS/MS similarity related to chemical similarity using MetGem software (Figure  S2 )  [13, 14]. Briefly, MS/MS data were converted to vectors, and cosine scores between each vector were calculated to evaluate the distance of structural homology. The t-SNE algorithm implemented in MetGem software was employed to generate the final distance map, called the molecular network.
The t-SNE allowed exploration of the dataset as a whole and preserved chemical proximity between clusters compared to the GNPS-like network. Metadata, such as the proposed chemical formula, retention times, relative areas of extracted ion peaks or activity of each extract, can be mapped on the MN using color mode. Once the molecular network was generated, the MS 2 libraries of standard molecules were queried, and known metabolites were annotated. Specific clusters related to P. sclerotiorum SNB-CN111 were enlightened by mapping strain identity on the network (Figure  S2 ). Using MS/MS database search engines, five MS/MS spectra were annotated as azaphilone analogs: sclerotiorin (1), sclerotioramine  (2) , ochrephilone (3), isochromophilone I (4) and isochromophilone VI (5) (Figures  S3-S7 ).
Penicillium Sclerotiorum Metabolism Dereplication To optimize the identification of minority compounds produced by P. sclerotiorum, a scale-up culture was performed. Bioguided fractionation was undertaken, leading to ten fractions whose antimicrobial activities were determined (Figure  S8 ), leading to 5 active fractions against Trichophyton rubrum with MICs below 32 µg. mL -1. In parallel, a t-SNE molecular network including bioactivity levels against Trichophyton rubrum was generated from LC-MS/MS data of the ten P. sclerotiorum fractions (Figure  1 ). Before isolation, a first annotation by querying MS/MS spectral databases was achieved. To clarify our molecular network feature annotation and to respect standards for reproducible science, annotation levels proposed by the Metabolomics Standards Initiative (MSI) were used  [23].
Several clusters of molecules in active fractions were thus annotated using MS/MS similarities (Figure  1 ). Subsequently, a second step in the dereplication process was carried out. First, molecules described in the Atlas of Natural Products as compounds 1 to 7 analogs were searched. Then, a second search was carried out on the Reaxys database on compounds structurally similar to molecules isolated from natural resources  [24]. Structures proposed through the literature review were annotated with level 3 using only exact mass and taxonomic information. Five molecules, i. e. , geumsanol A-C and G (14-17) and eupeniazaphilone C (18), were therefore annotated in cluster C, where hypocrellone  (7)  was first dereplicated  [25] [26] [27]. Two molecules, i. e. , isochromophilone IX  (6)  or penazaphilone F  (19)  and penazaphilone D  (20) , were also annotated in cluster B, including compounds 2 and 5  [28, 29].
In Silico Azaphilone Structure Prediction Using MS/MS Data and a t-SNE Molecular Network According to the first in silico dereplication steps, 4 distinct subclasses of azaphilone produced by the P. sclerotiorum SNB-CN111 strain were uncovered. In fact, these azaphilone subclasses presented one or several modifications of the same scaffold, including acylations on R 1 , a lactone ring at R 1 -R 2 , the presence of an oxygen or a functionalized nitrogen on Y, a diol or a methylethylene at R 3 -R 4 and finally chlorine or hydrogen at position X. (Figure  2 ). Isolated or synthetic azaphilones do not display all this combinatorial diversity in the literature  [21]. To further annotate the azaphilone-related metabolome from the P. sclerotiorum SNB-CN111 strain, all available chemical information, including MS/MS data, chemical formulas, biosynthetic pathways and literature surveys, was gathered and combined.