dataesr/scientific-paragraphs-categorization huggingface dataset:
org/10. 5281/zenodo. 4498941 7. 1. Thrusts of Amateur Astronomer Engagement Thrust 1: Awareness Because amateur astronomers will play such an important role in the optical observation of the next galactic supernova, SNEWS intends to strengthen its relationship with the global amateur astronomical community in the coming months and years. The reason is simple: astronomers can only participate if they know that SNEWS exists. This effort will involve reaching out to groups, attending conferences, and engaging with individuals on social media. Cultivating these community connections will be an ongoing process which will support the important goal of maintaining and maximizing observational readiness. Thrust 2: Preparedness To prepare the amateur astronomical community to respond quickly and effectively to a neutrino-indicated galactic supernova, it is critical to provide training and guidance regarding how to receive and interpret SNEWS alerts, and what to do when those alerts arrive.
Acknowledgements This work is supported by the  National Science Foundation  "Windows on the Universe: The  Era of Multi-Messenger Astrophysics" program : " WoU-MMA: Collaborative Research: A Next-Generation SuperNova Early Warning System for Multimessenger Astronomy " through grants  1914448 , 1914409, 1914447, 1914418, 1914410, 1914416, and  1914426 ; and via the " HDR-Harnessing the Data Revolution" program , grant  1940209. Participation of individual researchers has been supported by funds from the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie.
Furthermore, a large number of genomic datasets have been derived from mESCs, making them an ideal model for mechanistic studies of transcription. mESCs are maintained by provision of the cytokine leukemia inhibitory factor (LIF) in a fetal calf serum-containing medium (Serum-LIF)  (9)  or of inhibitors (i) of glycogen synthetase kinase 3 (GSK3) and mitogen-activated protein kinase kinase (MEK) in a chemically defined medium (N2B27)  (10). LIF, GSK3i, and MEKi stabilize a pluripotency TF network centered on OCT4, SOX2, and NANOG  (8). mESCs grown in the presence of the two inhibitors (2i) are called naïve and display higher and more homogeneous expression levels of pluripotency TFs than Serum-LIF-grown cells  (11). Naïve and Serum-LIF cell states are interconvertible  (12) , whereas naïve cells can efficiently differentiate into postimplantation epiblast-like cells (EpiLCs)  (13).
Immunofluorescence Cells were plated on a laminin-coated 96-well glass plate (Greiner Bio-One), fixed with PBS-4% PFA for 20 min, washed twice with PBS, permeabilized with PBS-0. 1% Triton X-100 for 10 min at room temperature (RT), incubated in the blocking solution (3% donkey serum (Sigma), 1% BSA in PBS-0. 1% Tween-20 (PBST)) for 1 h at RT, incubated with anti-Tfe3 antibody (RRID:AB_1857931, Cat# HPA023881, Sigma) 1/1000 in the blocking solution overnight at 4 C, washed three times with PBST, stained for 2 h at RT with the secondary antibody donkey anti-rabbit IgG-Alexa Fluor 555 (Life Technologies) 1/500 in PBST, counterstained with PBST-Hoechst33342 1/5000 (Life Technologies), washed twice with PBS, and imaged at an LSM-710 scanning head confocal microscope (Zeiss). Images were exclusively cropped with no further manipulation.
The data were acquired using 120,000 resolution for the peptide measurements in the Orbitrap and a top T (3s) method with HCD fragmentation for each precursor and fragment measurement in the ion trap according the recommendation of the manufacturer (Thermo Scientific). Protein identification and relative quantification of the proteins was done with MaxQuant version 1. 5. 3. 8 using Andromeda as search engine (71) and label-free quantification (LFQ, (72)) as described  (73). The mouse subset of the UniProt database from January 2017, containing 59,066 proteins, was used as reference. The protein and peptide false discovery rate (FDR) was set to 0. 01. MaxQuant version 1. 5. 3. 8. used additionally a decoy database of the same size and a contaminant database with 245 known contaminant proteins.
Strict trypsin cleavage was used allowing also cleavages N-terminal of proline, and a maximum of two missed cleavages were allowed. Carbamidomethylation of cysteine was set as a fixed modification, and oxidation of methionine and N-terminal protein acetylation were set as dynamic modifications. The peptide tolerance for the first search was 20 ppm and for the main search 4. 5 ppm, and the mass tolerance for the fragment ions was 0. 5 Da. The minimum length of the peptides was set to six amino acids and the maximum peptide mass to 8000 Da. MaxQuant results were loaded into Scaffold_4. 10. 0 (https:// www. proteomesoftware. com/). The LFQ values were analyzed with Perseus v. 1. 6. 2. 2 as follows: entries identified only by site or reverse and potential contaminants were removed, values were Log2-transformed, entries identified in less than two replicates in any group were removed, and missing values were imputed based on the normal distribution of each replicate with a 0.
Phylogeny Btb domain sequences were retrieved with the 'Architecture analysis' tool of SMART (smart. embl. de; query: "BTB AND ZnF_C2H2" in "Mus musculus") or individually with the 'Sequence analysis' tool. The phylogenetic tree was calculated, based on a multiple sequence alignment generated with T-Coffee (https://www. ebi. ac. uk/Tools/msa/tcoffee/), using the neighbor-joining clustering method provided by the ClustalW2 package (http://www. clustal. org/clustal2/) running 1000 iterations. The tree was visualized with iTOL (https://itol. embl. de/).
Gene expression analysis RNA-Seq reads and published data (see table below) were aligned to the mouse GRCm38/mm10 genome using qAlign from the Bioconductor package QuasR  (75) , with default parameters except for aligner="Rhisat2" and splicedA-lignment=TRUE. For aligning RNA-Seq reads for Dux overexpression  (17) , paired="fr" was additionally used. Alignments were quantified for known UCSC genes obtained from the TxDb. Mmusculus. UCSC. mm10. knownGene package using qCount. Microarray data  (33)  were analyzed and normalized using the Bioconductor package limma  (76). In this dataset, Tip60 KD replicate 3 is an outlier, and Ep400 KD replicates 1 and 3 do not show Ep400 transcript reduction and were therefore excluded from the analysis. Differential gene expression (Table  S3 ) was determined using edgeR  (77).
ChIP-seq analysis Published datasets (see table below) were aligned to the mouse GRCm38/mm10 genome using qAlign and profiled using qProfile from the Bioconductor package QuasR. 137435 ATAC peaks  (78)  were called using Macs2 (79), of which 22,826 were in promoters (±1 kb of annotated transcriptional start sites). For heat map visualization (Fig. S5D ), ChIP-seq signals were profiled in these promoters and ChIP enrichment calculated over respective inputs (Zbtb2, H3K4me3, Chd4, Mbd3, Dux) or controls (Kat5, Ep400). For metaplots (Fig. 5 , E and F and Fig. S5 , B and C), ChIP-seq signals were profiled in promoter regions of cluster genes that were extracted from the TxDb. Mmusculus. UCSC. mm10. knownGene Bioconductor package (https:// bioconductor. org/packages/release/data/annotation/html/TxDb.
Acknowledgments-We thank  Hans-Rudolf Hotz  ( Friedrich Miescher Institute [FMI] ) for help with the phylogenetic analysis;  Hubertus Kohler  ( FMI ) for cell sorting; Vytautas Iesmantavicius and  Jan Seebacher  ( FMI ) for help with computational analysis of mass spectrometry data and helpful discussions;  Verena Waehle.
Data availability The genomic data generated in this study have been deposited at ArrayExpress (E-MTAB-9796, E-MTAB-9797, E-MTAB-9798). The mass spectrometry proteomics data have been deposited at the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD025520 and PXD025466. Supporting information-This article contains supporting information.
). The DOX>Zbtb2 plasmid that cannot be detected by Mm01605943_g1 was produced by fusion PCR of the endogenous cDNA and the following gBlock (Integrated Statistical tests p-values presented in Figures 2I and 5B and Figs. S1D, S2D, S3G, and S5, F and G were calculated using two-tailed equal variance t-tests. Accession Description Reference GSE85632 DUX 18h, ChIP-seq (17) GSE101802 Zbtb2 2i, ChIP-seq (54) E-MTAB-6804 Mbd3 and Chd4, ChIP-seq (25) GSE67584 Kat5 and Ep400, ChIP-seq (62) E-MTAB-9453 ATAC-seq (78) DNA Technologies): ATGGATTTGACCAACCATGGACT TATTCTACTGCAGCAGTTAAACGCTCAGCGAGAGTTT GGTTTCCTGTGTGACTGCACGGTTGCAATCGGCGAT GTGTATTTTAAAGCCCATAAGAGTGTGTTGGCAAGTTT TAGTAACTATTTCAAAATGCTTTTCGTGCACCAAACA TCAGAGTGTGTGAGATTAAAACCAACAGATATCCAA CCAGATATCTTTTCATACTTATTGCATTTAATGTATA CCGGGAAGATGGCCCCACAGCTCATCGACCCTGTGAG GCTAGAGCAAGGGATCAAATTCCTGCACGCATACCCC CTCATCCAGGAAGCCAGCCTTGCCAGCCAAGGCAGCT TTTCCCATCCCGAGCAAGTCTTCCCTCTGGCCTCATC CTTGTACGGCATTCAGATTGCAGACCATCAGCTGAGA CAAGCCACCAAGATGAATTTAGGGCCTGAGAAACTTG GACGGGAGCCTAGGCCACAGGCATCCAGGATGA.
The construct was validated by plasmid qPCR with Mm01605943_g1. Accession Description Reference GSE85505 Kat5 knockdown, RNA-seq (61) E-MTAB-997 Mbd3 knockout, RNA-seq (22) GSE85632 DUX overexpression 24h, RNA-seq (17) GSE11243 Kat5 and Ep400 knockdown, microarray (33).
Intriguingly, various members of the C. gigasspecific NR1P subgroup are dynamically expressed during development  [17, 47]. In the ascidian tunicates C. intestinalis and Phallusia mammillata, a principal component analysis of NR transcripts has not yet been performed. However, the expression profiles of ascidian NRs available in the Aniseed database (https://www. aniseed. cnrs. fr/) allow at least some level of developmental clustering (Figure  4 ). Embryonic development is thus characterized by expression of TR2/4, RXR, PPAR and LXR, all of which are of maternal origin. EtL is associated with RAR, HNF4, GCNF and ROR expression, while peaks in the expression of THR, Rev-Erb, ERR, COUP-TF and PPAR occur during larval development  [53]. Altogether, these results demonstrate that the development of marine invertebrates is characterized by the stage-specific expression of NR subsets, suggesting that NR activity is correlated with distinctive developmental processes at different stages of the life cycle.
Acknowledgments: The authors are indebted to  Emma Bokobza  and  Manon Brunet , to the members of the  Ascidian BioCell and EvoInSiDe teams at the Laboratoire de Biologie du Développement de Villefranche-sur-Mer  as well as to the members of the  Canesi team of Environmental Physiology at the Università Degli Studi Di Genova  for fruitful discussions. The authors would like to thank  Laurent Gilletta  and  Régis Lasbleiz  of the  Centre de Ressources Biologiques Marines of the Institut de la Mer de Villefranche  that is supported by  EMBRC-France  and funded by the  Agence Nationale de la Recherche (ANR)  ( ANR-10-INBS-02 ).
e. , a score ≥6) PA habits before the lockdown. Models included a random intercept for participants and a random slope for linear time. Standardized beta coefficients (b) with 95% confidence interval (95% CI) are reported. Models were built using the lmerTest and lme4 packages  (Bates, M€ achler, Bolker, & Walker, 2015; Kuznetsova, Brockhoff, & Christensen, 2015) , in R software â (R Core Team, 2016). Second, the associations between PA habits, PA behaviours, and autonomous motivation across time were examined using path analysis  (Brown, 2006). Based on previous work  (Judah, Gardner, Kenward, DeStavola, & Aunger, 2018 ), a longitudinal model was computed and included all hypothetical pathways (see Figure  2A ). Regarding missing data, after conducting a Hawkins' test, there was no sufficient evidence to reject that values were missing at complete random (p =.
Acknowledgements H. L. W. and Y. W. contributed equally to this work. We thank  Dr. Marc-Antoine Stoeckel  for enlightening discussions. This work was financially supported by the MX- OSMOPED  project funded by the  FLAG-ERA programme , the  European Union  and the  Agence Nationale de la Recherche (ANR)  ( GA  No. ANR-17-GRF1-0006-04 ), the  EC Graphene Flagship Core 3 project  ( GA-881603 ), the  ANR  through the  Labex projects CSC  ( ANR-10-LABX-0026 CSC ) and  NIE  ( ANR-11-LABX-0058 NIE ) within the  Investissement d'Avenir program  ( ANR-10-120 IDEX-0002-02 ), the  International Center for Frontier Research in Chemistry (icFRC)  and the  Chinese Scholarship Council. The work in Mons is supported by the  Belgian National Fund for Scientific Research (FRS-FNRS ), within  FNRS-PDR-TOREADOR project  ( T.
0051. 18 ). Computational resources were provided by the  Consortium des Équipements de Calcul Intensif (CÉCI)  funded by  F. R. S. -FNRS  under Grant  F. R. S. -FNRS  under the conventions  Nº 2. 4. 617. 07. F and Nº 2. 5020. 11. DB is FNRS Research Director. Received: ((will be filled in by the editorial staff)) Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff)).
Climatic Data Climatic data were obtained from the French agency "Meteo France" (https://donneespubliques. meteofrance. fr/, accessed on 1 January 2019) for stations distributed between the three geographic origins: (i) southern-meso: three stations, (ii) southernsupra: three stations, and (iii) northwestern: nine stations (Table  2 ). For each of these stations, daily mean (Tmean) and minimal (Tmini) temperatures observed during the three coldest months (December-February) for three successive years (2015-2017) were extracted and averaged.
Statistical Analyses Statistical analyses were performed with the software R (version R-3. 6. 0) and its working environment Rstudio (Version 1. 1. 453). The intraspecific differentiation was tested on each of the three thermal tolerance indices (CTmin, CCT, and AR) using two complementary approaches-an hypothesis test approach and a model comparison approach realized with linear mixed effects models (LME models). Two sets of explanatory variables were investigated separately:.
Pintureau. Lower part: Genbank references. Table  S2 : Summary of the statistical models evaluated in the study. According to the selection of the random effects, the nature of the model was either a linear mixed effects model (LME) or a linear model (LM). For each of the traits, the two approaches are presented, i. e. , the test hypothesis ("Origin" as the sole fixed effect) and the model comparison. In this second case, the null model and the 31 possible combinations obtained from the two meteorological variables (t_mean and t_mini) and the geographic ones (alt, lat, and lon) are provided. The model minimizing the Information criteria (cAIC for LME and AIC for LM) is indicated in bold.
Acknowledgments: We are grateful to the members of the  Biological Resources Center "Egg Parasitoids Collection " (doi. org/10. 15454/AY4LMT ), in particular to  Anael Marchand  for the rearing of Trichogramma populations and  Geraldine Groussier  for helpful suggestions. The  BRC EP-Coll  is a part of  BRC4Env , the pillar " Environmental Resources " of the  Research Infrastructure AgroBRC-RARe. We are also grateful to  Vincent Calcagno  and  Victor Burte  for the loan of the video equipment as well as the related help. We are grateful to the three anonymous reviewers who helped in improving the manuscript.
The level of labeling was followed by MS analysis and biotinylation was carried on until unlabeled protein was no more detected. Biotinylation was stopped by addition of 1 M Tris buffer. Protein Characterization: SDS-PAGE analysis was performed with precast gel (NuPage 4-12% Bis-Tris gels). Purified proteins were characterized by MS using an Ultraflex MALDI TOF/TOF apparatus (Bruker Daltonics, GmbH). Proteomic analysis was performed following in-solution digestion procedures (Pierce Trypsin Protease, MS Grade, Thermo Fisher Scientific). Mass spectra were analyzed by using Peptide Analysis software (Bruker Daltonics, GmbH) and protein identification was carried on by using Peptide Mass (ExPASy server)  [29]  and a custom-built database. SFS Protein Isolates: SFS isolates were provided by LRGP (Nancy, France) and produced from the cold press meal (OLEAD, Pessac, France) as previously described with minor modifications.
Acknowledgements This project received financial support from SOFIPROTEOL as part of FASO  PRODIAL  project. JA, SH and KAP participated actively to the  COST Action FA1402 -IMPARAS (Improving Allergy Risk Assessment Strategy for New Food Proteins ). The authors thank  Sara Albe-Slabi  and  Odile Mesieres  from  LRGP  and  Mbalo Ndiaye  from  AVRIL  for the production of SFS protein isolates. The authors thank  Victoria Pineau  for management of the  PRODIAL  project. We thank  Celine Chapuis  for technical assistance.
Data processing All metabolic rates were integrated over the duration of the experiments using trapezoidal integrations, and the relative changes (in %) in tanks D and G as compared to the controls (average between C1 and C2) were computed as follows: Relative change = Rate Treatment -Rate Controls Rate Controls ×100, (7) where Rate Treatment is the integrated rate measured in treatments D and G (D1, D2, G1 or G2) and Rate Controls is the averaged integrated rates between the duplicate controls (treatment C). Daily rates of 14 C-based production were computed from hourly rates assuming a 14 h daylight period. As incubations performed from samples taken at t0 (before dust addition) do not represent what happened in the tanks between t0 and t24 h, as a first assumption, we considered a linear evolution between these rates and those measured from samples at t24 h and recomputed an average value for the time interval t0 -t24 h.
As a consequence of a stronger sensitivity of heterotrophic prokaryotes to temperature and/or pH, the ongoing warming and acidification of the surface ocean will result in a decrease in the dust fertilization of phytoplankton in the coming decades and a weakening of the atmospheric CO 2 sequestration capacity of the surface oligotrophic ocean. The contrasting results obtained at the three stations during our study will need to be translated into process parameterization. The important dataset presented in this paper, covering a variety of tested waters, environmental stressors and responses, will allow such a parameterization to be used in biogeochemical models coupled to ocean dynamics in order to depict the spatial and temporal dynamics of stocks and fluxes following dust deposition in surface oligotrophic waters.
Acknowledgements. The authors thank the captain and the crew of the  R/V Pourquoi Pas ? for their professionalism and their work at sea. Céline Ridame  and  Kahina Djaoudi  are thanked for their help during sampling,  Sophie Guasco  and  Marc Garel  for their help in ectoenzymatic measurements on board. Financial support. This study is a contribution to the  PEACETIME  project, a joint initiative of the MERMEX and ChArMEx components supported by  CNRS-INSU ,  IFREMER ,  CEA , and  Météo-France  as part of the programme MISTRALS coordinated by  INSU. PEACETIME is a contribution to SOLAS and IMBER international programmes. The project was endorsed as a process study by  GEOTRACES. The project leading to this publication has also received funding from the  European FEDER Fund  under project  1166-39417.
Julie Dinasquet  was funded by a  Marie Curie Actions-International Outgoing Fellowship  ( PIOF-GA-2013-629378 ). The project leading to this publication has received funding from the  European FEDER Fund  under project  1166-39417. The research of  Emilio Marañón  and  Maria Pérez-Lorenzo  was supported by the  Spanish Ministry of Science, Innovation and Universities  through project  POLARIS  (grant no. PGC2018-094553B-I00 ) and by the  European Union 's  Horizon 2020 research and innovation programme  through project  TRIAT-LAS  (grant no. 817578 ). Review statement. This paper was edited by  Silvia Becagli  and reviewed by four anonymous referees.
We have recently used DNA combing to demonstrate that transcriptional activity modulates origin distribution in the 616-and 996-kb-long CCSER1 and DMD genes, respectively, in chicken DT40 cells, and proposed that transcription favours efficient initiation at the 5 and 3 ends of large genes at the expense of the gene body  (46). Here, we reanalyzed these datasets and extracted the orientation of each replication track to compute the RFD profiles of inactive and active DMD, as well as those of CCSER1 when it is transcribed at wild-type (WT) levels or overexpressed. In order to compare SM-and population-based approaches, we also generated the RFD profiles of DMD and CCSER1 in WT cells using Okazaki fragment sequencing (OK-seq)  (11, 14). We show that DNA combing-and OK-seq-based RFD profiles are highly concordant, with DNA combing-based RFD successfully reproducing domains of replication composed of two zones of initiation flanking a zone of termination detected by OK-seq.
RFD profiling by OK-seq RFD profiling of DT40 cells by OK-seq was performed as described in  (11)  and  (14). Briefly, asynchronously growing cells were labelled with 20 M 5-ethynyl-2 -deoxyuridine (EdU, Jenabioscience) for 2 min. After DNA purification and heat-denaturation, Okazaki fragments were sizepurified on sucrose gradients, labelled with biotin at EdU sites using click-chemistry, captured on magnetic beads coated with streptavidin and amplified by PCR. Sequencing was performed on HiSeq (Illumina) at the IB2C highthroughput sequencing platform (Gif-sur-Yvette, France). Aligned OK-seq data (BAM files) were imported in R  (52)  as GenomicAlignments, and reads were converted into Ge-nomicRanges. RFD was computed using R as the difference between rightward-and leftward-fork coverage normalized by total coverage.
Single molecule-based RFD and coverage profiles of DMD and CCSER1 loci The length of rightward-and leftward-replicated DNA was determined as described in Figure  1A  for each DNA fibre with replication signals spanning DMD, DMD Tet , CCSER1 or CCSER1 β a obtained by combing and schematized in Supplementary Figure  4  of  (46) ; examples of raw DNA combing data with replication and FISH signals on a DNA molecule are shown in Supplementary Figure  3  of  (46). Coordinates, length and directionality of replicated DNA were compiled using Microsoft Excel. Coverages of rightward-(R) and leftward-replicated DNA (L) along DMD and CCSER1 loci were determined and RFD was computed for each position as follows: RFD = (R -L)/(R + L) using custom R scripts. Initiation and termination zones were annotated by manually scanning the profiles.
Subsampling of OK-seq data For subsampling, 2500 reads mapping on the locus of interest (either DMD or CCSER1) were selected randomly from the OK-seq dataset with the highest number of reads and used to compute RFD and coverage profiles using bins of 10 kb. This was performed 100 times and the 100 subsampled profiles are presented in Figure  4C , D along with the RFD and coverage from the corresponding single molecule data with the same binning interval.
FUNDING Ligue Nationale Contre le Cancer ;  Association pour la Recherche sur le Cancer ;  Agence Nationale de la Recherche  [ ANR-15-CE12-0011-01 ,  ANR-18-CE45-0002 ,  ANR-19-CE12-0028-01 ];  Fondation pour la Recherche Médicale  [FRM DEI201512344404];  Cancéropole Ile-de-France  [ PLBIO16-302 ]; work of the C. L. C. team is supported by the  YPI program of Institut Curie; ATIP-Avenir program  from  Centre National de la Recherche Scientifique  and  Plan Cancer ;  Agence Nationale de la Recherche [Re-DeFINe , TELOCHROM];  Institut National du Cancer (INCa)  [ PLBIO19-076 ]. Funding for open access charge:  ANR  [ ANR-18-CE45-0002 ].
DATA AVAILABILITY OK-Seq data for DMD and CCSER1 loci in DT40 cells have been deposited at NCBI's Gene Expression Omnibus (GEO) repository and are accessible under accession number GSE164252. The datasets generated during the current study, including bed files with DT40 OK-seq reads for WT CCSER1 and DMD loci, oriented tracks, initiation and termination events from DNA combing analyses, as well as custom R scripts are available from the github repository (https://github. com/LacroixLaurent/DT40 RFD).