dataesr/scientific-paragraphs-categorization huggingface dataset:
This model was computed for each bin and a Bonferroni correction of 5% was applied to the p value. Significant bins were extracted corresponding to 22,670 genomic regions (overlapping significant bins were merged). 3,080 regions were obtained after filtering the 22,670 regions based on a log 2 FC > 2 or < À2 between D10 and D1(Table  S2 ). Allocation to pre-defined chromatin states was as in Pintacuda et al. 64  (https://github. com/guifengwei/ ChromHMM_mESC_mm10). The categories ''Enhancer'', ''Weak Enhancer'' and ''Strong Enhancer'' were merged into a single category ''Enhancer''. ESC super-enhancers genome coordinates were taken from Whyte et al. 45  The liftOver webtool from the UCSC website (https://genome. ucsc. edu/cgi-bin/hgLiftOver) was used to convert the mm9 track bed file to an mm10 bed file.
Prior to analyses, DNA libraries were checked for quality and quantified using a 2100 Bioanalyzer (Agilent). The libraries were sequenced on an Illumina HiSeq4000 sequencer as paired-end 100 base reads following Illumina's instructions. Image analysis and base calling were performed using RTA 2. 7. 3 and bcl2fastq 2. 17. 1. 14. Processing ChIP-seq data Libraries were aligned using bowtie2  67  with default parameters on mouse and fly genomes together. All alignments were filtered on MAPQ (mapping quality value) 30 with SAMtools. 73 Libraries were deduplicated with the Picard toolkit. 87 The number of reads mapped on the fly genome was used as a spike-in value to downsample libraries as previously described. 88 Peak calling was performed with MACS2 66 with default parameters. For the SUMO1 ChIP-seq data, low coverage peaks were filtered out.
The pileup values were extracted from the MACS2 output, transformed using log 10 and scaled. Peaks with a scaled log 10 value inferior to À0. 5 were filtered out from each replicate. This corresponded approximately to the 33% quantile (À0. 52, À0. 53, À0. 48, À0. 55, for D1 rep1, D1 rep2, D8 rep1, D8 rep2, respectively). An irreproducible discovery rate (IDR)  74  of 0. 1 was used to filter out irreproducible peaks. For each histone mark or SUMO1 ChIPseq dataset, the IDR validated peaks from all time points were merged using the bedtools merge function. 89  differential analysis was performed for the SUMO1 ChIP-seq data by counting the number of reads for each peak in each downsampled replicate with the featureCounts program. 90 The produced matrix was analyzed with the DESeq2 R package,  68  using a size factor of 1 for the 4 libraries (2 rep D1, 2 rep D8).
Gene enrichment analysis The EGSEA R package  76  was used for gene list enrichment with Gene Ontology term (GO term), pathways (KEGG, Biocarta) or curated gene list (mSigDB) with the egsea. ora function (Over-representation Analysis). Bulk RNA-seq Total RNA was purified by Trizol extraction and RNA was analyzed on a BioAnalyzer Nano chip (Agilent). If the RNA integrity number was superior to 8, samples were used for subsequent analyses. RNA concentration was quantified with a Qubit (Invitrogen). Total RNA-seq libraries were generated from 500 ng of total RNA using TruSeq Stranded Total RNA Library Prep Gold kit and TruSeq RNA Single Indexes kits A and B (Illumina), according to manufacturer's instructions. Briefly, cytoplasmic and mitochondrial ribosomal RNA (rRNA) were removed using biotinylated, target-specific oligos combined with Ribo-Zero rRNA removal beads.
Surplus PCR primers were further removed by purification using AMPure XP beads (Beckman Coulter #A63881) and the final cDNA libraries were checked for quality and quantified using capillary electrophoresis. The libraries were sequenced on an Illumina HiSeq4000 sequencer as paired-end 50 base reads following Illumina's instructions. Image analysis and base calling were performed using RTA 2. 7. 3 and bcl2fastq 2. 17. 1. 14. Processing bulk RNA-seq data FastQC (Version 0. 11. 2) was run using the following arguments -nogroup -casava to produce base quality, base sequence content and duplicated reads. FastQ-Screen (Version 0. 5. 1) was run using the following arguments: -subset 10000000 -aligner bowtie -bowtie '-p 2 0 x. In order to avoid PCR amplification biases in read quantification, duplicated reads were removed using the MarkDuplicates tool of Picard.
ACKNOWLEDGMENTS We greatly thank  Laure Bailly-Cuif  for suggestions and insightful comments. We acknowledge  Jacob-Sebastian Seeler  for helpful discussions and rereading of the manuscript. We acknowledge  Heather Marlow ,  Francois Spitz , and  Baptiste Saudemont  for assistance with initial scRNA-seq experiments. We are grateful to  David M. Suter  ( Swiss Federal Institute of Technology, Switzerland ) for the Sox1:EGFP-T:mCherry double reporter ESCs and  Pablo Navarro  ( Institut Pasteur, Paris ) for the LF2 ES cell line. We thank  Andrey Aristov  for help with image analysis,  Sandrine Schmutz  and the  Cytometry and Biomarkers Platform (Institut Pasteur, Paris)  for cell sorting,  Juliana Pipoli Da Fonseca  and the  Biomics Platform (Institut Pasteur, Paris)  for scRNA sequencing, as well as  Samy Gobaa  and the  Biomaterials and Microfluidics Platform (Institut Pasteur, Paris)  for microfluidic chip manufacturing.
Sequencing for ChIP-seq/RNA-seq/Methyl-seq was performed by the GenomEast platform, a member of the '' France Genomique''consortium  ( ANR-10-INBS-0009 ). This work was supported by grants from  European Research Council (AdG SUMiDENTITY) ,  Agence Nationale de la Recherche  ( ANR-19-CE12-0011-01 ), Odyssey, and  LNCC (Equipe labellisee)  (to A. D. );  Region Île-de-France DIM-ELICIT  (to C. N. B. ); the  Helmholtz Association and German Research Foundation (DFG)  Project -ID 213249687 (SFB 1064 ) (to M. -E. T. -P. ); and the  Novo Nordisk Foundation  ( NNF14CC0001 ) and the  European Union Horizon 2020  ( EPIC-XS-823839 ) (to M. L. N. ).
Validation of chimeras is also a challenge because we expect low alternative allele frequency since the variant will appears on only one haplotype and one cell layer representing a small proportion of leaf tissue. Sanger sequencing has been found to be limited when alternative allele frequency is under 15-20%  [43]  but other technologies such as Molecular Inversion Probes (MIP)  [44]  has proven to be efficient in this particular condition  [45]. Widely used in medical programs to detect rare diseases  [46] [47] [48]  it has also been used in plants to detect pathogens  [49]  or assist in genomic selection  [50]. Because of repetitive sequences, it is also difficult to design specific target sequences that only capture the identified SNVs, MIPs along with MIPGEN designing software  [51]  is efficient on this specific criteria, it also has the advantage of being performant with a small amount of DNA (200ng) which also makes it useful in forensic applications  [52, 53].
Because of the sequencing technology and the high performance of long read assembly, we obtain longer chromosomes compared to PN40024. v4. Chromosome lengths are very similar between leaves and roots but trio-binning which correctly phase the assembly, there is a slight difference between Merlot-hap-CF and Merlot-hap-MG (Fig. 2 ). From 1,6% to 2,8% of the assembly can't be accurately placed in the pseudo-molecule. It is mainly small highly repetitive sequences that are found in different places throughout the genome. This proportion was not attributed to a specific location and couldn't be associated to a contig. The results of Benchmarking Universal Single-Copy Orthologs (BUSCO) using embryophyta lineage-specific databases  [59]  are resumed in Table  5. Results show that up to 98. 7% of genes searched are found in the pseudomolecules and nothing is lost compared to the raw assembly.
Validation of chimeras by MIPs Out of the 104 positions identified as chimeras, MIP-GEN software was able to design a ligation and extension probe for 95 target regions. MIPseq was performed on Merlot_leaf and Merlot_root samples. Amplification was obtained for 86 positions but only 32 had enough depth to compare both samples (Table  10  and Additional files 2 and 3). 22 positions have the expected alleles on each sample and validate with enough depth PacBio results. 8 positions have the expected alleles but also have a single BUSCO was first performed on raw reads of each haplotype, just after trio-binning (Raw), second BUSCO was done on the pseudomolecules (Pseudo). Complete means that the gene is entirely found, whereas fragmented is not. Single copy means the gene is only found once whereas duplicated means it is found several times.
Assembly and building pseudo-molecules DNA consensus call sequences obtained under BAM format were converted to fastq using bam2fastq tool from SMRTLink v9. 0. 0 PacBio library. The HIFI sequencing DNA quality was verified using FastQC version 0. 11. 7. Figure  5  illustrates the whole bioinformatics workflow to build pseudo-molecules and transfer annotation. Paternal and maternal kmers were identified using the parental reads with yak-0. 1 software. The outputs were then used in hifiasm-0. 13 with default parameters to bin 'Merlot' long reads and assemble both haplotypes. This was done on both organs (leaf and root). For each haplotype, contigs were aligned on PN40024. v4 using minimap2 version 2. 17  [65]. Best contigs alignments were used to build an AGP file and from there reconstruct each pseudo-molecule.
In order to refine the pseudo-molecules, we then reexecuted the same process starting with an alignment of each haplotype on the other previously reconstructed. The embryophyta_odb10 lineage from BUSCO 5. 3. 1 software was carried out in genome mode to estimate the completeness of all assemblies  [59]. BUSCO was also performed on protein sequences using "prot" option, protein sequences were obtained from the pseudomolecules by using gffread tool version 0. 12. 6 with default parameters. Liftoff 1. 6. 1 tool with default parameters was used to transfer the annotation of PN40024 Vcost. v3 reference genome to the pseudomolecules  [60].
Chimera detection Reads were mapped on Merlot-root-hap-CF and Merlotroot-hap-MG pseudomolecules with Minimap2 version 2. 17  [65]  with the option -x map-hifi and variant calling was performed with DeepVariant software version 1. 1. 0  [66]  using PacBio model and default parameters. Finally filtering variants was done with vcftools 0. 1. 16 version  [67]. Chimera detection was processed by filtering vcf from Merlot-leaf on Merlot-root pseudomolecule. We only conserved variants with more than 10 depth coverage, "PASS" quality flag and genotype quality (GQ) over 20. Non homozygous positions on all other sequences were excluded. Repeated sequences were identified by building a specific 'Merlot' library with repeatmodeler/2. 0. 2a-bin  [68]  and then using repeatmasker/4. 1. 1 software  [69] , and all chimeras in repeated sequences were excluded.
Acknowledgements We are grateful to the members of the  AGAP-DAAV  team and especially  Charles Romieu , for their help and their contribution in discussions. We acknowledge  Laurent Torregrosa  for his help in understanding grapevine cell layer physiology. We sincerely thank the  Petrus  team:  Jean-Claude Berrouet ,  Olivier Berrouet ,  Fabienne Caillon ,  Lionel Caillon ,  Gilles Rabeyroux ,  Michael Paiva ,  Pierre-Jean Dalesme ,  Johann Ventre ,  Catherine Gaillard  and  Emilie Verral  for their support during this work. We would like to thank  Cécile Marchal  and  Sandrine Dedet  for supplying samples from INRAE grapevine collection  Vassal-Montpellier. We highly acknowledge the work of  Carine Satgé  from  Toulouse CNRGV  for adapting the DNA extraction protocol to grapevine.
Data Availability Raw reads of PacBio sequencing are available on the European Nucleotide Archive repository, under the project named PRJEB59893: https://www. ebi. ac. uk/ena/browser/view/PRJEB59893. Contigs, AGP file, chromosome scale assembly and annotations transferred from PN40024 VCost. v3 with liftoff are available on Recherche Data Gouv: https://doi. org/10. 57745/OJ07SN. PN40024 sequence and annotation used in this study are available on INTEGRAPE platform, https://integrape. eu/resources/genes-genomes/ genome-accessions/. Merlot clone 343 is available at INRAE, domaine de Vassal, under the code 0Mtp2399.
In this work, we examine internal rTIO-AMOC teleconnections diagnosed from observational data sets, following the methods of previous literature, to show how the two ocean basins are interconnected. We also take the opportunity to "zoom out", analyzing in detail which ocean regions and tropical ocean basins contribute to the tropical temperature gradients that govern the rTIO-AMOC mechanisms from the literature. We then analyze model simulations to understand the robustness of the internal rTIO-AMOC connection. Finally, we draw conclusions on the importance of these internal mechanisms in the face of anthropogenic global warming, offering alternative interpretations of SST-based observed AMOC fingerprints in the context of internal variability.
RESULTS Observed tropical ocean SST teleconnections to AMOC Direct observation of the influence of tropical ocean temperature gradients on AMOC is inhibited by a lack of long-term direct AMOC observations  29, 30. In this context, a North Atlantic SST index was proposed as a proxy for AMOC fluctuations  10, 31. This index is defined as the mean extended winter (November-May: NDJFMAM) difference between North Atlantic subpolar gyre and global mean SST  31. It projects onto the annual mean AMOC 31. Since SST has been observed and reconstructed for much longer than AMOC, we here invoke this observed SST-based AMOC index (henceforth SST AMOC index; SST AMOC ) to reconstruct AMOC variations and examine the tropical ocean-AMOC teleconnection mechanism in observational data sets. Furthermore, the objective of this study is to investigate the internal variability, separate from the anthropogenic forced signal.
The full observed TIO, rTIO cov and SST AMOC all display pronounced decadal-scale variability, as well as an accelerating trend over the later decades due to external forcing of the climate system (Fig. 1a-c ). This includes both the unforced internal and the forced signal. A trough and peak are observed in 1890 and 1910 in rTIO cov , and in 1910 and 1930 in SST AMOC. We find a temperature drop and subsequent recovery between 1920 and 1960 in rTIO cov and a substantial drop between 1940 and 1980 in Fig. 1  Temperature time series for the Tropical Indian Ocean and the SST AMOC index, as well as their cross-correlations in reanalysis data sets. Time series of the observed ERSST (black) and decomposed into the forced (red) and unforced internal (blue) components for the (a) tropical Indian Ocean (TIO) sea surface temperature (SST), (b) relative tropical Indian Ocean SST weighted by covariances (rTIO cov ), and (c) AMOC SST-fingerprint index SST AMOC.
The years used for each observational data product is from 1871 to 2013. SST AMOC. The observed TIO and rTIO cov indices show a significant negative correlation with SST AMOC when the latter leads by approximately 20 years (Fig. 1d , e; r = -0. 92 and -0. 60 respectively, solid black). To improve our understanding of the underlying connections, we deconstruct the observed signal into a forced (red) and an unforced (blue) component. The forced TIO and rTIO cov signal are steadily warming, while forced SST AMOC cools. The latter suggests AMOC weakening over time (red lines in Fig. 1a-c ) related to anthropogenic climate change (as also argued in  31  ). It is worth noting that, as estimated from the SST AMOC proxy with forcing estimation from climate models, this study cannot make any statements about the emergence of AMOC weakening in observations.
Disentangling the different drivers of AMOC variability is an exciting scientific question for the future. To conclude, we have analyzed several observational data sets as well as global climate model simulations from the CMIP6 archive to establish a pathway by which lowfrequency internal, i. e. , unforced, changes of tropical ocean temperature gradients may influence AMOC several decades later, leading to AMOC multi-decadal variability. This pathway includes shifts in geostationary waves that impact the NAO, changing surface winds with impacts on salinity and temperature distributions that change Ekman pumping, impacting AMOC. In models, internal rTIO cov warming strengthens AMOC ~20 years later. We also find that across model simulations a SST-based index that was proposed to indicate forced AMOC changes  10, 31  is reflective of internal AMOC changes ~6 years earlier, allowing a tracking of AMOC changes using relatively easily observable surface water characteristics.
ACKNOWLEDGEMENTS This research is supported by the  ARCHANGE  project of the " Make our planet great   again" program  ( ANR-18-MPGA-0001 , France). Additional support is provided to AVF by  NSF  ( AGS-2053096 ) and  DOE  ( DE-SC0024186 ). JM is also supported by the  ANR-19-JPOC-003   JPI climate/JPI ocean ROADMAP  project. This study benefited from the  ESPRI (Ensemble de Services Pour la Recherche l 'IPSL) computing and data centre (https:// mesocentre. ipsl. fr) which is supported by  CNRS ,  Sorbonne University ,  Ecole Polytechnique  and  CNES  and through national and international grants. LFB received funding by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation)  under Germany's  Excellence Strategy ,  EXC 2037  " Climate, Climatic Change and Society" CLICCS  (project no.
390683824 ), as a contribution to the  Center for Earth System Research and Sustainability (CEN) of Universität Hamburg. LFB and MM are also supported through the  ANR-TREMPLIN ERC  Project  HARMONY , Grant Agreement Number  ANR-20-ERC9-0001. We acknowledge the  World Climate Research Programme , which, through its  Working Group on   Coupled Modelling , coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies who support  CMIP6  and  ESGF.
DATA AVAILABILITY The data that support the findings of this study are all available within the data repositories detailed below. The defined subpolar North Atlantic region from Caesar et al. , 2019 is available from http://www. pik-potsdam. de/~caesar/ AMOC_slowdown/. The HadiSST data set is available at Met Office, Hadley Centre (https://www. metoffice. gov. uk/hadobs/hadisst/). The COBE SST and NOAA ERSST data sets are available at NOAA Earth System Research Laboratory's Physical Sciences Division (https://www. esrl. noaa. gov/psd/data/gridded/data. cobe. html; https://www. esrl. noaa. gov/psd/data/gridded/data. noaa. ersst. v5. html). Information for CMIP6 data is available from the World Climate Research Programme (WCRP) webpage (https://esgf-index1. ceda. ac. uk/projects/cmip6-ceda/) and the data for this experiment is archived on the IPSL cluster in Paris, France (https://esgfnode.
Funding information Programme Grants for Applied Research, Grant/Award Numbers:  ICA-CDRF-2017-03-53 ,  NIHR-301322 ,  NIHR-CDF-2017-10-037 ,  NIHR302390  funded by an  NIHR   Advanced Fellowship Award  ( NIHR-301322 ). Audrey Lamouroux is funded by  Nîmes and Montpellier University Hospital  mobility grant. This article presents independent research funded by the  NIHR and Nimes and Montpellier University Hospital  and the views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR  or the  Department of Health and Social Care. The funder of the study played no role in the study design, data collection, data analysis, data interpretation, or writing of the report.
8 - 8 Figure adapted from Dawood et al. 2021 with permission. 71 SIMCOCK ET AL. 10970223, 0, Downloaded from https://obgyn. onlinelibrary. wiley. com/doi/10. 1002/pd. 6361 by University College London UCL Library Services, Wiley Online Library on [24/05/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Wider adoption of these investigations will increase the societal awareness surrounding miscarriage and reduce the emotional cost for parents by investigating their loss in a more acceptable personalised manner. ACKNOWLEDGMENTS Ian C. Simcock is funded by a National Institute for Health Research (NIHR) (ICA-CDRF-2017-03-53 and NIHR302390), Owen J Arthurs is funded by an NIHR Career Development Fellowship (NIHR-CDF-F I G U R E 7 Step-wise flowchart for non-invasive investigation of perinatal loss including options for micro-CT and high-field magnetic resonance imaging (MRI).
Introduction Modern systems are currently monitored by multiple sensors that generate large volumes of data. As an example, for a Boeing 787, approximately 1000 parameters are continuously monitored for the engine, leading to a total of 20 terabytes of data per flight hour  [1]. Using such datasets, several AI algorithms have been developed in the past years to estimate the Remaining Useful Life (RUL) of components and systems  [2]. The integration of data-driven RUL prognostics into maintenance planning has been shown to significantly reduce maintenance costs and the number of failures  [3] [4] [5]. However, most existing studies focus either on developing RUL prognostics only, or on developing predictive maintenance planning models where the probability of failure is based on simple, generic probability distributions of the time-tofailure/RUL  [6].
Description of the dataset In this study, we consider the aircraft turbofan engines in the C-MAPSS dataset  [23]. In C-MAPSS, the degradation of engines is simulated using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) program developed by NASA. The C-MAPSS dataset consists of four subsets: FD001, FD002, FD003 and FD004. In turn, each subset consists of a training and a test set. For each engine in the training set, one measurement per sensor per flight cycle is generated, from the installation of the engine until the failure (i. e. , run-to-failure instances). In the test set, one measurement per sensor per flight cycle is generated as well. However, the sensor measurements stop at some time before failure. The goal is to predict the RUL at that moment, i. e. , the number of flight cycles until the engine fails.
Hyperparameter tuning The considered hyperparameters of the neural network are in Table  2 , optimized with as starting point the hyperparameters of the CNN in  [4, 24]. In contrast with these papers, the window size equals for all four data subsets. For data subsets FD002 and FD004, however, some test instances do not have 30 historical flight cycles. For these test instances we apply zero padding  [29] , i. e. , we set all the sensor measurements of the missing flight cycles to zero. This technique is very common in image processing. Moreover, we use a piece-wise linear RUL target function  [24, 30, 31]  with 𝑅 early = 125 flight cycles, i. e, the target RUL is 𝑅 early = 125 flight cycles when the actual RUL is larger than flight cycles.
Mean RUL prognostics Table  3  shows the Root Mean Square Error (RMSE)  [33]  with the mean RUL prediction (see Eq. (  13 )) for the four data subsets. The RMSE of the mean RUL prediction is a higher for subset FD002 and FD004, probably due to the multiple operating modes. Table  3  also shows the results of existing studies employing various machine learning algorithms for the same dataset. The performance of our RUL prognostic method with respect to the mean RUL is comparable to the state-of-the art solutions, especially for data subset FD002 and FD004. Potential contributing factors to the good performance of our approach are that we consider larger sizes of the window for FD002 and FD004 than most existing studies, and that we normalize the measurements with respect to the operating conditions (see Eq.