dataesr/scientific-paragraphs-categorization huggingface dataset:
Modelling RBD-ACE2 interaction We have modelled the RBD-ACE2 interface with the S477N and E484K substitutions using the final refinement step of  HADDOCK 2. 4 (van Zundert et al. , 2016). We used the crystal structure of ACE2 (19-615) bound to SARS-CoV-2 RBD (PDB ID: 6m0j; Lan et al. (  2020 )) as a starting point and introduced the substitutions using UCSF ChimeraX  (Goddard et al. , 2018). We used default parameters for refinement with extended molecular dynamics (MD) simulation (steps for heating phase: 200, steps for 300K phase: 2500, steps for cooling phase: 1000).
These phylogeographic analyses ran for a total of 125 (continent-level) and 200 million (country-level) iterations, with the Markov chain being sampled every 25,000th iteration, in order to reach an effective sample size (ESS) for all relevant parameters of at least 200, as determined by Tracer 1. 7  (Rambaut et al. , 2018). We used TreeAnnotator to construct a maximum clade credibility (MCC) tree and used baltic (https://github. com/evogytis/baltic) to visualise it. Since more B. 1. 620 genomes are turning up every day, the latest dataset we were able to compile for from Cameroon to yield a dataset with 182 genomes total. A maximum likelihood phylogeny was inferred from this dataset using PhyML  (Guindon et al. , 2010)  under the HKY+Γ 4 model of nucleotide substitution  (Hasegawa et al. , 1985; Yang, 1994)  which was then rooted on the reference sequence and shown in Figure  S4.
Figures 2, 3, and S4 included 121 B. 1. 620 genomes. Most new genomes in this dataset compared to the core dataset described earlier were sequences from Germany and Lithuania. These 121 genomes were combined with previously described representatives of VOCs B. 1. 1. 7, B. 1. 351, P. 1 and B. 1. 526. 2, other lineages circulating in Lithuania (B. 1. 177. 60 and B. 1. 1. 280), and high quality SARS-CoV-2 genomes.
FigureS4. Shared SNPs of investigated lineages. SNP alignment of multiple high-quality genomes from Cameroon, available genomes of lineage B. 1. 620 as well as earliest and latest genomes of lineagesB. 1. 1. 7, B. 1. 351, P. 1, B. 1. 526. 2, B. 1. 177. 60, B. 1. 177. 57, and B. 1. 1. 280. Only SNPs shared by at least two genomes are shown, lineage B. 1. 620 outlined with a dashed line. Sites identical to the reference (GenBank accession NC 045512) are shown in grey, changes from the reference are indicated and coloured by nucleotide (green for thymidine, red for adenosine, blue for cytosine, yellow for guanine, dark grey for ambiguities, black for gaps). If mutation results in an amino acid change, the column label indicates the gene, reference amino acid, amino acid site, and amino acid change in brackets.
SNPs shared by at least two genomes are shown, lineage B. 1. 620 outlined with a dashed line. Sites identical to the reference (GenBank accession NC 045512) are shown in grey, changes from the reference are indicated and coloured by nucleotide (green for thymidine, red for adenosine, blue for cytosine, yellow for guanine, dark grey for ambiguities, black for gaps). If mutation results in an amino acid change, the column label indicates the gene, reference amino acid, amino acid site, and amino acid change in brackets. Maximum likelihood phylogeny on the right shows the relationships between depicted genomes and was rooted on the reference sequence. Tip circles indicate each genome's country of origin in the same colour scheme as Figures 3 and 2.
ACKNOWLEDGMENTS We gratefully acknowledge the authors from originating laboratories responsible for obtaining the specimens, as well as submitting laboratories where the genome data were generated and shared via GISAID, on which this research is based. An acknowledgment table with accessions of SARS-CoV-2 genomes used here is included. We thank all involved in the collection and processing of SARS-CoV-2 testing and genomic data, as well as associated metadata on individual travel histories. In particular we would like to thank  Marc Noguera Julian ,  Elisa Martro Catala ,  Samuel Cordey ,  Piet Maes ,  Keith Durkin ,  Lize Cuypers ,  Lien Cattoir ,  Veerle Matheeussen ,  Vincent Enouf ,  Sylvie van der Werf ,  Etienne Simon-Lorière ,  Tobias Schindler ,  Vladimira Koudelakova ,  Gabriel Gonzalez ,  Ariane Düx ,  Yanthe Nobel ,  Livia Patrono ,  Justas Dapkūnas , and  Andrew J.
Figures 2 , 2 Figures 2, 3, and S4 included 121 B. 1. 620 genomes. Most new genomes in this dataset compared to the core dataset described earlier were sequences from Germany and Lithuania. These 121 genomes were combined with previously described representatives of VOCs B. 1. 1. 7, B. 1. 351, P. 1 and B. 1. 526. 2, other lineages circulating in Lithuania (B. 1. 177. 60 and B. 1. 1. 280), and high quality SARS-CoV-2 genomes.
). Lithuania EPI_ISL_802863 B. 1. 177. 60 England EPI_ISL_950607 B. 1. 177. 57 Cameroon EPI_ISL_845553 Cameroon EPI_ISL_845554 Cameroon EPI_ISL_1190757 Cameroon EPI_ISL_1190752 Cameroon EPI_ISL_1715194 NC_045512 21/24.
, 2018; Yuet al. , 2019; Eban-Rothschild et al. , 2016). Here we identified positive Rev-erbα interactions within the first kilobase upstream of the transcription start site of the Fabp7 promoter, but not in the Fabp3 or Fabp5 promoters (Fig. 1A-C ). The top 20 Rev-erbα binding site loci, peak score, distance to the translational start site and gene names are listed in Table  1. Gene Ontology (GO) analysis revealed significant enrichment of several biological processes, molecular functions, and cellular components (Table  2 ) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis shows the top 20 pathways in Rev-erbα ChIP-seq genes (Fig. 2 ). The complete list of Rev-erbα ChIP-seq genes is provided in [SUPPLEMENTAL dataset 1]. To confirm that Rev-erbα binds the Fabp7 promoter in multiple brain regions, we compared Rev-erbα binding in the Fabp7 promoter against the negative control insulin, and the positive control NPAS2 in WT and Rev-erbα KO mice.
ChIP-seq and cistromic analysis. For ChIP-seq, material from three mice was pooled prior to library generation. ChIP DNA was prepared for sequencing according to the amplification protocol provided by Illumina, using adaptor oligo and primers from Illumina, enzymes from New England Biolabs and PCR Purification Kit and MinElute Kit from Qiagen. Deep sequencing was performed by the Functional Genomics Core (J. Schug and K. Kaestner) of the Penn Diabetes Endocrinology Research Center using the Illumina HiSeq2000, and sequences were obtained using the Solexa Analysis Pipeline. Sequenced reads were aligned to the mouse reference genome (mm9) and peak calling was performed with HOMER  (Heinzet al. , 2010). ChIP-seq data are deposited in NCBI GEO GSE67973  (Zhang et al. , 2015) , for GSM1659684 and GSM1659685 datasets.
GO and KEGG analysis Gene ontology analysis was performed on the ranked list of Rev-erbα ChIP-seq genes with peak score >2 [SUPPLEMENTAL dataset 1], using Panther GO-Slim against the mouse gene list (http://geneontology. org release 2021-01-01: 44,091;  (Ashburneret al. , 2000; The Gene Ontology resourc, 2021). Top non-redundant categories are presented. KEGG pathway analysis was performed on the same gene list using KEGG Mapper https://www. genome. jp/kegg/tool/map_pathway1. html (Kanehisa and Sato, 2020)  against mouse pathways.
Article CASYâ TT-cell counter measurement. 1*10 6 cells/ml were seeded in Advanced RPMI medium with 20% FBS, 1% penicillin/streptomycin, 0. 1% gentamycin, 2. 5% HEPES and 1% Glutamax. The first 4 days, cells were cultured with 100 ng/ml Stem cell factor (SCF) and 100 ng/ml FMS-like tyrosine kinase 3 ligand (FLT3L). Afterward the growth factors were exchanged with 20 ng/ml IL-5. Half of the medium was exchanged every other day and on day 8, the cell culture flask was exchanged. On day 11 or 12, cells were harvested and checked for the eosinophil purity using flow cytometry. The purity of the eosinophils was analyzed using anti-SiglecF-PE, -APC or -APC-Cy7 antibodies and flow cytometry was performed using a BD FACS Canto system and data was subsequently analyzed using the FACS Diva 5. 1 software.
1% gentamycin, 1% L-glutamine, 10% FBS and 20 ng/ml of murine recombinant IL-5 in a 24-well plate. 10 min before analysis, 5 mM Sytox Orange was added. A heating chamber attached to the confocal microscope LSM 710 from Zeiss was pre-heated. Cells were set in focus and 5,000 MF were added to the culture. Samples were analyzed using a LSM 710 from Zeiss with the Zen2. 0 software or the Axio Observer 7 from Zeiss with the Zen2. 6 software. For Z stacks taken with the Axio Observer the iterative deconvolution was performed. Analysis of the eosinophils entering ETosis was done for 3 independent images taken randomly from the slides. Therefore, the cell counter plugin from the ImageJ software was used. Cells with extracellular DNA were assigned as cells, which had entered ETosis, while cells with roundish appearance, intact donut-shaped nuclei and no extracellular DNA-net structure were considered as cells, which had not entered ETosis.
MACS Eosinophils from BALB/c and mMT mice were isolated by positive selection and isolated from the pleura and small intestine according to the manufacturer's protocol. Briefly, cells were blocked with Fc-block (PBS/ 1% BSA+ 1/1,000 diluted rat IgG), stained with 1% anti-SiglecF-PE, followed by incubation with anti-PE beads and isolated by magnetic separation. For the gut cells, 5% FCS was added to all reagents. Cell counts were determined using CASY TT. Purity of the anti-SigleF-PE antibody labeled cells was determined following MACS using the BD FACS Canto system and data was subsequently analyzed using the FACS Diva 5. 1 software. The purity of the eosinophils isolated from the thoracic cavity was around 98. 4%, while gut eosinophils were enriched with a purity of 30%.
ACKNOWLEDGMENTS We thank  Ms. Vanessa Schmitt  and  Prof. Dr. Christoph Wilhelm  from the  Institute of Clinical Chemistry and Clinical Pharmacology, University Hospital Bonn , for their support obtaining gut eosinophils;  Ms. Laura Eiden  from the  Institute of Chemical Biology and Chemical Genetics, Life and Medical Science Institute (LIMES) Bonn , for support during confocal microscopy; and  Johanna Scheunemann  and  Indulekha Karunakaran  from the  Institute for Medical Microbiology, Immunology, and Parasitology, University Hospital Bonn , for critical reading of the manuscript and helpful discussions. We thank all staff members of  Elanco Animal Health -Research and Exploratory Development, Germany , which were involved in the maintenance of the D. immitis lifecycle and blood collection.
Furthermore, we would like to thank all staff members of the  NIH/NIAID   Filariasis Research Reagent Resource Center  ( FR3 ;  College of Veterinary Medicine, University of Georgia, Athens, GA, USA ) for all of their support and discussions, especially on the protocol optimization of the life-cycle maintenance of D. immitis. This project was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under  Germany's Excellence Strategy -EXC -390873048 , the  Excellence Cluster Immunosensation (DFG ,  EXC 1023 ), and  HU 2144/1-1  awarded to M. P. H. A. E. , J. J. R. , and B. C. B. received a fellowship from the  J€ urgen Manchot Foundation. M. P. H. and A. H. are members of the  German Center for Infection Research (DZIF) , and A. H. is a member of the  Excellence Cluster Immunosensation (DFG ,  EXC 1023 ).
45-200 µm, representing picoplankton (0. 45-3 µm), small and large nanoplankton (3-10 and 10-50 µm, respectively), and microplankton (50-200 µm). In addition, vertical net hauls were taken from 50 m depth to the surface at selected stations. The net hauls were fractionated into the large nanoplankton (10-50 µm) and microplankton (50-200 µm) fractions. From the plankton samples DNA was extracted, the V4 region of the 18S rRNA-gene was amplified by polymerase chain reaction (PCR) with universal eukaryote primers, and the amplicons were sequenced by Illumina high-throughput sequencing. Sequences were clustered into amplicon sequence variants (ASVs), representing protist genotypes, with the dada2 pipeline. Taxonomic classification was made against the curated Protist Ribosomal Reference database (PR 2 ).
g. ,  de Vargas et al. , 2015). In recent years, several metabarcoding studies of protist communities in the Arctic Ocean have been undertaken, but most represent only snapshots of the community as based on a single cruise or season (e. g. ,  Bachy et al. , 2011; Kilias et al. , 2014; Monier et al. , 2015; Vader et al. , 2015). Studies that have sampled the full yearly cycle have typically only sampled the upper water column (0-50 m depth) (e. g. ,  Marquardt et al. , 2016). Here we present a metabarcoding dataset from the northern Svalbard region of the Arctic Ocean sampled during five cruises representing the full seasonal cycle and at three to four depths from the surface down to 1000 m. Metabarcoding targeted the V4 region of the 18S rRNA gene. The data are provided both as raw reads and as amplicon sequence variants obtained after processing with the dada2 pipeline, with corresponding ASV abundance tables.
Study area and general environmental conditions The physical and biogeochemical oceanographic conditions during these cruises have previously been described in detail in  Randelhoff et al. (2018). Flow-cytometric counts of viruses, bacteria, and pico-and nanoplankton have been published in  Sandaa et al. (2018). We briefly describe the meth-ods and reiterate the main results here to provide background for our metabarcoding data. The complete dataset from  Randelhoff et al. (2018)  can be found at the PAN-GAEA Data Publisher for Earth and Environmental Science (https://doi. org/10. 1594/PANGAEA. 884255, Paulsen et al. , 2017). The reader is advised to consult the original papers for detailed descriptions of the methods. The environmental parameters included here are listed in Table  1.
Cell counts FCM analyses were performed using an Attune Focusing Flow Cytometer (Applied Biosystems by Life Technologies) with a syringe-based fluidic system and a 20 mW 488 nm (blue) laser. The samples were fixed with glutaraldehyde (0. 5 % final conc. ) at 4 • C for a minimum of 30 min, flashfrozen in liquid nitrogen, and stored at -80 • C until analysis. Autotrophic phytoplankton was categorized as the cyanobacterium Synechococcus, picophytoplankton, or nanophyto-Table  1. Overview of the environmental data available from the water samples corresponding to the metabarcoding DNA samples presented in this paper, listed in the file "env_data_depths. txt" (see "Code and data availability" section). The original dataset can be found in  Paulsen et al. (2017).
The material on each nylon mesh was collected with sterile filtered seawater in a 50 mL Falcon tube and collected by filtration on a polycarbonate filter (10 µm pore size 47 mm diameter, Millipore). The filters were transferred to cryovials to which 1 mL of warm AP1 buffer was added, flash-frozen in liquid nitrogen and kept at -80 • C until DNA extraction. The size fraction < 10 µm passing through the nylon mesh system was fractionated into the 3-10 and 0. 45-3 µm size fractions by serial filtration through 142 mm diameter polycarbonate filters as described above. Points indicate the depths where samples for protist metabarcoding were taken with Niskin bottles. To better distinguish between data points in the epipelagic zone, the y axis is square-root-transformed. The full profile was not available from all stations.
Bioinformatics processing PhiX sequences were removed, and the raw reads were sorted according to the Illumina index by the Illumina software at the sequencing provider. For the HiSeq datasets, the samples within each Illumina library were demultiplexed with cutadapt v2. 10 with Python 3. 6. 11 (Martin, 2011) , requiring 0 errors in the internal barcodes. The amplification primers were removed with cutadapt v2. 8 with Python 3. 7. 6, with setting -trim-n (trim N's on ends of reads). The reads were denoised and merged with dada2, v1. 16  (Callahan et al. , 2016). For the HiSeq reads the settings were truncLen = c(240,200), minLen = c(240, 200), truncQ = 2, maxEE = c(10, 10), and max_number_asvs = 0. Chimeras were detected with isBimeraDenovo with default settings and removed with removeBimeraDenovo, with "method_chimera" = "pooled".
For the MiSeq reads truncLen and minLen were set to c(270, 240), and the other settings were the same as for HiSeq. The reads were subsequently classified with assignTaxonomy, the dada2 implementation of the naive Bayesian classifier method  (Wang et al. , 2007) , against the Protist Ribosomal Reference Database, version 4. 12. 0 (https: //github. com/pr2database/pr2database/releases/tag/v4. 12. 0, last access: 25 January 2021;  Guillou et al. , 2013). ASVs with less than 90 % bootstrap value at class level and/or which comprised fewer than 10 reads in total were removed. As this study is focusing on the protists, all reads assigned to Metazoa and Viridiplantae (Embryophyceae) were excluded from the processed ASV tables (Table  4 ).
Subsampling to equal read number was performed 100 times, and the average read number per ASV was used, rounded to 0 decimals. Subsampling was done with the function rrarefy() from the "vegan" package  (Oksanen et al. , 2020) , v. 2. 5-7. The low number of protist reads in the 50-200 µm fraction was due to a high proportion of Metazoan reads in this fraction. To calculate the ASV richness of each sample, the subsampled ASV table was transformed to presence-absence. An overview of the available versions of the ASV table is given in Table 4. To assess whether the sequencing depth was sufficient, we plotted rarefaction curves for each DNA sample and calculated the slope at the endpoint of the curve with the functions "rarecurve" and "rareslope" respectively. Figures were made with the R package "ggplot2"  (Wickham, 2016).
Overview of sequenced samples In total we obtained 44 water samples from Niskin bottles and 8 net hauls, which were fractionated into 140 and 15 size-fractionated samples, respectively (the DNA isolation of the 10-50 µm fraction from the net haul taken at station P04 in May failed). The samples from Niskin and net hauls are referred to as DNA samples in the following, and they are denoted month_station_depth_minfract_maxfract or month_station_net_minfract_maxfract, respectively. On some DNA samples, we performed replications of DNA extraction, PCR with variable annealing temperature, and/or replicate sequencing. Thus, one or more fastq-file pairs can map onto the same DNA sample. The fastq files were deposited individually to the European Nucleotide Archive (ENA, project accession number PRJEB40133) and are referred to as a "sequencing event" ("seq_event") in Table  3 ).
Total number of reads and ASVs After quality filtering, dada2 processing, removal of chimeras, and non-target taxonomic groups, the dataset comprised 6536 protist ASVs, corresponding to 32 164445 reads. After subsampling to equal number of reads per sample within each size fraction, the dataset was reduced to 6430 ASVs and 5 729 358 reads. Number of ASVs per division or class within each size fraction, after subsampling, is shown in Table  A1. In total, we recovered 3339, 2720, 2799, 1153, and 3172 ASVs in the 0. 45-3, 3-10, 10-50, 50-200, and 3-180 µm size fractions, respectively. Note that the numbers are not directly comparable, as the fractions were not obtained from the same number of samples (e. g. , 3-180 µm were only sampled in January and March). Syndiniales and Dinophyceae had the highest number of assigned ASVs, with 2166 and 1723, respectively.
Code and data availability The fastq files with raw 18s rRNA V4 reads are available on the European Nucleotide Archive repository under project number PRJEB40133. The untransformed ASV table, metadata table, and a table with environmental data are deposited in the Sea scientific open data publication repository (SEANOE), under the CC-BY license, with DOI https://doi. org/10. 17882/79823 (Egge et al. , 2014). The ASV tables, including the ASV sequences and assigned taxonomy; R-scripts for producing the figures and tables; and a Shiny application with interactive versions of the figures and tables are deposited on GitHub: https://github. com/ EEgge/micropolar_protists_datapaper (last access: 30 August 2021; https://doi. org/10. 5281/zenodo. 5517980, Egge and Vaulot, 2021a). The Shiny app can be found at https: //micropolar-protists.
Conclusions This dataset offers novel insights into the spatial and seasonal diversity and taxonomic composition of the protist community in the Atlantic gateway to the Arctic Ocean. It is the first study to provide data on the eukaryote microbial community throughout a complete year and down to 1000 m in this area of the Arctic. It forms the basis for future studies to detect changes in the eukaryote community and for more detailed studies on the dynamics and community structure of specific taxonomic groups. Table  A1. Number of ASVs assigned to each division or class, distributed by size fraction, and in total. Note that a given ASV may occur in multiple size fractions. Number of samples in each size fraction: 0. 45-3 µm: 44; 3-180 µm: 18; 3-10 µm: 26; 10-50 µm: 33; and 50-200.
Acknowledgements. We wish to thank members of the MicroPolar and  Carbon Bridge  projects for assisting in the sampling campaigns and the crews at K/V Svalbard (January cruise),  R/V Lance  (March), and  R/V Helmer Hanssen  (May, August, and November cruise). Financial support. The study was conducted as part of the  Research Council of Norway  project  Micropolar -Processes and Players in Arctic Marine Pelagic Food Webs -Biogeochemistry, Environment and Climate Change  no. 225956/E10. Daniel Vaulot  was supported by  ANR  contract  PhytoPol  ( ANR-15-CE02-0007 ). Review statement. This paper was edited by  David Carlson  and reviewed by two anonymous referees.
JCI There are currently very limited therapeutic options for patients with MDS, and no therapies have been identified with selective activity in cohesin-mutant disease. We found that cohesin mutations cause a 70-fold increased sensitivity to PARP inhibition and are a potential biomarker of PARP inhibitor sensitivity in cohesin-mutant myeloid malignancies. PARP inhibitors are currently approved by the FDA for treatment of breast and ovarian cancer in the context of germline BRCA mutations and have been previously tested in a phase I study of unselected patients with advanced hematologic malignancies  (45). The effect of talazoparib monotherapy in cohesin-mutated AML or MDS with excess blasts is under investigation in a pilot proof-ofconcept study (ClinicalTrials. gov identifier NCT03974217).
(G) Structured illumination microscopy of SMC1A and PARP1 in WT (U937 WT-1, WT-2) and STAG2-knockout (U937 STAG2-KO-3, STAG2-KO5) cells. Fluorescence signal displayed alone and merged with the nuclear Hoechst stain. Quantification of colocalization of SMC1A with PARP1 or RPA1 was determined using Manders colocalization coefficient. Original magnification, 100×. Box and whiskers represent mean ± Tukey's. *P < 0. 0001, unpaired Student's t test. JCI Insight 2021;6(3):e142149 https://doi. org/10. 1172/jci. insight. 142149 (23). See Supplemental Methods. Raw and processed data have been deposited at figshare: https://figshare. com/articles/dataset/CRISPR_screen_of_isogenic_STAG2_mutant_cell_lines/7120796. Western blotting, immunoprecipitation, and mass spectrometry. Details of Western blotting, immunoprecipitation, and IP-MS performed using iTRAQ and TMT6 labeling are detailed in the Supplemental Methods.