dataesr/scientific-paragraphs-categorization huggingface dataset:
Due to the lack of observational constraints for Dimorphos, this particular shape was selected for the DRA based on the observed elongations of the secondary component of other binary systems  (Pravec et al. 2016). Finally, cases ab1. 2bc1. 3 and ab1. 2bc1. 1 were selected due to their strong attitude-stability properties according to GUBAS rigidbody simulations and to see whether the rubble-pile codes reproduce the same predicted stability. Since the attitude-stability properties of Dimorphos are driven primarily by its own shape, accounting for the oblate shape of Didymos is not necessary, allowing us to simply treat it as a point mass. The set of PKDGRAV runs presented in this subsection performs a given simulation two times: once as a rigid body in which all the constituent particles of Dimorphos are locked together into a rigid aggregate; and once using PKDGRAVʼs SSDEM package where Dimorphos is treated as a rubble pile.
This work was supported in part by the  DART mission ,  NASA  Contract # 80MSFC20D0004  to  JHU/APL. F. F. acknowledges funding from the  Swiss National Science Foundation (SNSF) Ambizione  grant No. 193346 , and the  National Centre for Competence in Research PlanetS  supported by the  Swiss National Science Foundation. P. M. acknowledges funding support from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 870377  (project  NEO-MAPP ), from the  French space agency CNES  and the  European Space Agency (ESA). We kindly thank the reviewers, whose comments significantly improved the manuscript. Some of the simulations herein were carried out on The University of Maryland Astronomy Departments YORP cluster, administered by the  Center for Theory and Computation.
An initial report on the application of this approach to activation time estimation can be found in  [5, 9]. Here we report on improvements to this method, validate it using simulated data and invasive EGM recordings from a canine preparation, and demonstrated its utility in a clinical case, with particular attention to performance on recovery times. We compare our results to the widely used standard temporal derivative method. Our implementation of the spatiotemporal estimation of activation and recovery times is freely available from the Github code repository at https://github. com/jcollfont/ activationtimes.
Animal experiments To provide a much more realistic test, we applied both methods to in-vivo ECGI data acquired in canine experiments as described previously  [13]. Briefly, ECGI was used to obtain noninvasive EGMs at the epicardial surface in three dogs, during normal rhythm and ventricular pacing. Simultaneously, invasive recordings of EGMs were obtained from 100 implanted electrodes on the epicardium. For this study, the activation and recovery times determined from the high-quality invasive recordings by the temporal method served as the "ground truth" reference, while the ECGI-reconstructed EGMs were used to test activation and recovery time estimates using both the temporal-only and spatiotemporal method. As these were normal dogs, the invasive recordings contained hardly any fractionation and thus activation/recovery time computation for the ground truth data was relatively straightforward.
Torso-tank data In a separate data set, explanted hearts from pigs were analyzed in the presence of recovery-altering drugs as described previously  [16, 17]. In short, an explanted pig heart was put in a Langendorff perfusion inside a torso-shaped plastic tank. 256 electrodes on the tank surface recorded 'body-surface' potentials and served as input to an ECGI procedure to reconstruct potentials on the submerged heart's surface inside the tank. 108 electrodes in a sock wrapped around the heart provided ground-truth EGMs. Dofetilide and pinacidil were infused locally in the heart to induce recovery changes. We report on data from 18 beats from 4 pig hearts under varying drug conditions.
Animal data Fig. 3  shows a comparison of the temporal-only method with the spatiotemporal method for a single beat in the animal data. The colored heart surface reflects the result of these methods, while the colored circles represent the recorded activation/recovery times from the implanted electrodes. For the spatiotemporal method, results are shown for different values of the spatial area s and temporal window t. Qualitatively, the temporal-only method resulted in sudden local changes in the activation and recovery isochrones (i. e. , high gradients) that seemed not to be present in the EGMs obtained by the implanted electrodes. The spatiotemporal method seemed to be less sensitive to these effects. For example, the activation isochrones computed with the temporal-only method showed a crowding of isochrones (suggesting a local activation delay) between the dark red and light blue regions.
Torso-tank data In the torso-tank data with explanted pig hearts and drug-induced recovery changes, the spatiotemporal method did not improve the activation or recovery times significantly (activation times: median R = 0. 67 vs R = 0. 64, p = 0. 06); recovery times: R = 0. 78 vs R = 0. 81, p = 0. 18 for the temporal vs. spatiotemporal method, respectively). However, the spatiotemporal method did remove some regions with local outliers. This is illustrated by the isochrones in Fig. 6A , where the top-left region with noisy electrograms (shown in Fig. 6B ) resulted in a false annotation by the temporal method which was corrected by Fig. 3. Ventricular activation isochrones (left) and recovery isochrones (right) for a single, paced beat, as reconstructed with ECGI (colored surface) and invasively recorded (colored circles), illustrating the effect of different parameter combinations s and t.
Alternative implementations of ECGI operate on reconstructed transmembrane potentials instead of unipolar electrograms, and seem less prone to inherent spatial smoothing of the inverse problem  [3]. We note again that a software implementation of our method is freely available on Github (https://github. com/jcollfont/activationtimes) and that there is an on-going cooperative effort towards improved activation and recovery time estimation as part of the CEI Activation and Recovery Time workgroup (see http://www. ecg-imaging. org/). The CEI workgroup provides multiple datasets for comparison of different approaches. We encourage interested readers to try our method on their own data and to participate in the CEI workgroup's activity.
Funding Matthijs Cluitmans is supported by the  Hein Wellens Fonds , and a  Veni grant  from the  Netherlands Organization for Scientific Research  ( TTW 16772 ). Paul Volders is supported by The  Netherlands Heart Foundation  ( NHS2007T51 ) and a  Vidi grant  from the  Netherlands Organization for Scientific Research  ( ZonMw 91710365 ). Dana Brooks was supported by the  Center for Integrated Biomedical Computing  through the  National Institute of General Medical Sciences  of the  US National Institutes of Health  under grant numbers  P41 GM103545  and  R24 GM136986 , and  Burak Erem  and  Jaume Coll-Font  were both also supported by  P41 GM103545  for part of their work on this project.
169 In this study, we expand on previous results by including samples from more northern 170 areas of the species range (Kansas and Oklahoma) and by examining the phylogeographic and 171 demographic history of P. cornutum using both mtDNA sequences and thousands of nuclear 404 There was a relatively large effect of including/excluding sample KK104 on divergence times. 405 Assuming a mutation rate of 0. 0008, calibrated divergence times under the MSci model were 406 4. 83 Ma for node T and 7. 68 Ma for node S. Introgression times were 1. 78 Ma for  A =  B and 407 213 Ka for  C =  D (see Fig. 7  for node labels). We provide calibrated estimates for the non- 408 admixed Peak 1 data set only, as that is our best estimate of the evolutionary history of these 409 populations. 440  Myers et al.
e. morphology), and dense 660 population sampling, especially throughout Mexico. The evolutionary history presented here 661 highlights the importance of both hard and soft allopatric forces in shaping a species through 662 gene flow, as the lineage divergences appear at least partially influenced by a changing habitat 663 and environmental niche. Finally, this study should serve as a foundation for the exploration of 664 powerful new models of demographic inference that make use of genomic data sets. S1 ). A single P. solare individual from 674 Pima County, Arizona was also included as an outgroup taxon. All new collections were 675 approved by the IACUC Committee at Miami University (protocol number 992_2021_Apr).
Data were 685 demultiplexed using Illumina bcl2fastq v. 2. 17. 1. 14. Two samples (FHSM16593, FHSM16898)  686 were excluded from further analysis due to a low number of reads. All nGBS data were 687 uploaded to the SRA (accessions provided upon acceptance). 892 variation between the three populations based on results from our phylogenetic and population 893 structure analysis. However, MOMENTS is based on the approximation of the discrete Wright-894 Fisher Model, meaning that it is not appropriate to pool populations that may be genetically 895 distinct (e. g. Plains cluster). Therefore, we used the same individuals in MOMENTS as in BPP 896 analysis. For each data set (with and without KK104), we tested 10 3D models that were based 897 on various aspects of divergence previously hypothesized for species in the region ranging from 898 simple models with no gene flow to more complex models involving multiple time periods and 899 varying degrees of gene flow between populations (Fig.
904 The program easySFS (https://github. com/isaacovercast/easySFS) was used to 905 determine the dimensions that would maximize segregating sites shared between samples 906 when creating the folded JSFS; we also retained one SNP per locus to minimize linkage 907 disequilibrium. MOMENTS is an efficient method of simulating the evolution of an allele 908 frequency spectrum over time using differential equations. The basis of MOMENTS is similar to 909 the diffusion approximation approach utilized in the program ∂a∂i and many of the models we 910 tested were adapted from previously developed ∂a∂i and MOMENTS models  (Gutenkunst et al. , 911 2009; Leaché et al. , 2019; Portik et al. , 2017). For all models, we performed consecutive rounds 912 of optimization with multiple replicates using the best scoring parameter (highest log-likelihood) 913 estimates to base searches in the subsequent round  (Portik et al.
, 2017; Leaché et al. , 2019). 914 Default settings in moments_pipeline (https://github. com/dportik/moments_pipeline) were used 915  (replicates = 10, 20, 30, 40; maxiter = 3, 5, 10, 15; fold = 3, 2, 2, 1) , and we optimized 916 parameters using optimize_log_fmin, a simplex (a. k. a. amoeba) method in terms of log 917 parameters. Optimized parameter sets of each replicate were used to simulate the 3D-JSFS,.
Differential analysis using the limma package  (15)  highlighted that the abundance of 235 mRNAs in heavy polysomes and 218 in light polysomes was significantly changed upon glucose shift (adjusted p-value < 0. 05 and logFC > 0. 5, Fig 1C  and 1D ), but not in monosomes (Fig. 1E ). These changes do not correlate with a significant difference in mRNA total level (Fig. 1B ). As expected from the global increase in translation we observed in high glucose (Fig. 1A ), most of the identified transcripts were enriched in polysomes (Fig. 1F ): 164 in the heavy polysomes, 131 in the light polysomes, 49 common to both. Thus, only a minority of transcripts, 58 in total, were downregulated. Interestingly, from the 16 transcripts increased in RNA abundance (Fig 1B ) only four have been found increased in polysomal fractions (Fig.
The most translated transcript of each of these 604 genes was analysed by our in-house developed software (detailed in Materials and Methods) to retrieve sequence information from ENSEMBL database and identify structural features. Several characteristics appear in the statistical analysis of the features (Fig. 3B -F and Supp. Fig. 3 ). Among them, the minimum folding energy normalized over the length of the sequence (MFE per BP) is an interesting measure to estimate the complexity of an mRNA untranslated region (UTR) structure. Transcripts with higher translation ratio difference between high and low glucose appear to have statistically significant shorter open reading frames (ORFs), and shorter and less complex UTRs. Conversely, transcripts that are down regulated have longer ORFs and more structured 3'UTR since their MFE per BP distribution slightly decreases (Fig.
The difference between these two clusters comes from the fact that the decrease is observed in light polysomes for cluster 4 and in heavy polysomes for cluster 5. Based on the link between features and behaviours that we observed (Fig. 3 ), we hypothesized that the different patterns observed for the clusters could be a consequence of cis-regulatory elements present on the mRNAs which could serve as binding sites for trans-acting factors such as RNABPs and miRNAs. To investigate this possibility, we calculated a series of mRNA features for the most abundant transcript for each gene. We developed an RNA feature extraction tool (see Material and Methods) that is able to download, from the ENSEMBL database bioMart  (20) , the transcript sequences with additional annotations and calculate sequence and structural properties (see Supp.
Clustering reveals that specific pathway regulators are co-regulated in response to glucose, and that TOP motifs are preponderant to upregulate the translation machinery components. Next, we performed gene ontology enrichment analyses, using the R package ClusterProfiler  (23) , in order to assess if the mRNAs belonging to the different clusters were implicated in specific biological processes. Cluster 1 and 6 were enriched for categories related to translation but especially for ERrelated translation (Fig. 6A-B ). Cluster 4 (Fig. 6C ), which contained the translationally repressed mRNAs in high glucose, was enriched in mRNAs coding for proteins involved in regulation of metabolic processes and cell death. In particular, genes involved in tricarboxylic acid, acyl-CoA, and thioester metabolisms were found to be enriched in cluster 4.
Mapping, read counts and TPM calculation Quality controlled reads were then mapped to the human genome (GRCh38 from Ensembl 92) using STAR 2. 6 (54) by using the parameter --quantMode GeneCounts to generate gene counts tables. STAR aligner was further instructed to generate an output (--quantMode TranscriptomeSAM) suitable as an input for RSEM  (55). RSEM reports tables with transcript per million (TPM) for genes and mRNA isoforms  (56). For all the rest of downstream analyses, the tables were filtered to retain only the genes which are annotated as protein coding in the Ensembl 93 annotation tables.
Gene counts table were transformed in log CPM (Counts per Million base). Genes whose CPM values were smaller than 1 at least in one sample were discarded. Then a customized R function further filtered genes whose coefficient of variation (defined as the ratio of the standard deviation over the mean) within replicates was lower than 0. 75 and the mean CPM expression was higher than 4. As a first step for quality control of our datasets we performed a hierarchical clustering analysis by using the TPM tables for each sample. Clustering was performed on the Euclidean distance matrix and the Ward's minimum variance method was used for forming clusters (option Ward. D2 in the hclust function of R).
Differential expression analysis and clustering Analyses were performed using the limma (15) Bioconductor package. Differentially expressed genes (DEGs) or translated genes (DTGs) were identified by fitting linear models between all the pairs of the three polysome profile fractions applying the ebayes method to calculate p-values. Only genes with adjusted p-values for multiple testing ≤ 0. 05 were selected. A separate list containing the TPM (transcripts per million reads) values was kept for downstream analysis. The average expression of each gene was calculated in each fraction (monosomes, light polysome and heavy polysomes) and condition (low or high glucose). Then for each fraction and gene the logarithmic ratio of means of high glucose over low glucose was calculated (log ratio of mean TPM expression).
mRNA features collection We developed an in-house software for the retrieval and calculation of an array of sequence features for a given set of genes, The RNA features extraction tool is freely accessible in the GitHub repository (https://github. com/parisepigenetics/rna_feat_ext). The tool searches for either the most well annotated transcript for each gene in the gene list or it can also choose the most expressed transcript if a transcript abundance file is provided (e. g. the output of tools like RSEM or StringTie). We used the latter possibility using the average of all the polysomal samples. For each transcript, the tool extracts different mRNA features using the bioMart API. The mRNA features that we extracted were: length of the 5'UTR, CDS and 3'UTR and the GC content of both 5' and 3' UTR.
The software also calculates the folding free energy for the 5' and 3' UTR (by using the RNAfold algorithm of the Vienna package (57), normalized by the length (MFE per bp) that is a measure of the stability and the complexity of the RNA secondary structure, an "in-house" devised TOP-mRNA local score and the Codon Adaptation Index (CAI, (58)), based on the codon usage of human genes. All statistical analyses of the features distributions along different translation behaviours were conducted by the groups based statistics R package ggstatsplot (https://cran. r-project. org/web/packages/ggstatsplot/index. html) using the Kruskal-Wallis H-test for comparing independent samples.
DATA AVAILABILITY Raw sequencing data files and processes count tables for all transcripts/genes are available on Zenodo : DOI 10. 5281/zenodo. 4279599. The RNA features extraction tool, our inhouse software for the retrieval and calculation of an array of sequence features for a given set of genes is freely accessible in the GitHub repository: https://github. com/parisepigenetics/rna_feat_ext. The R-code for all the analyses in this work is available here: https://github. com/parisepigenetics/Translatome_Bcells_glucose.
R, Bernhart SH, Höner zu Siederdissen C, Tafer H, Flamm C, Stadler PF, et al. ViennaRNA Package 2. 0. Algorithms for Molecular Biology. 2011;6(1):26. 58. Sharp PM, Li WH. The codon Adaptation Index--a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res. 11 févr 1987;15(3):1281-95. 59. Yu G, Wang L-G, Yan G-R, He Q-Y. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics. 15 févr 2015;31(4):608-9. 60. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 4 janv 2016;44(D1):D457-62. 61. Fabregat A, Korninger F, Viteri G, Sidiropoulos K, Marin-Garcia P, Ping P, et al. Reactome graph database: Efficient access to complex pathway data.
PCR products were puri ed using ExoSAP-IT, sequenced using a BigDye terminator, puri ed using Sephadex G50 and then Sanger Sequencing analyzed using an automated sequencer (ABI 3730; Applied Biosystems, Foster City, CA). Generated Data was analyzed using SeqScape version 3. 2 and BioEdit Sequence Alignment Editor Version 7. 2. 5. Variants found in our study were described using the recommendations of the Human Genome Variation Society 'HGVS'  [27] , and interpretations were based on the American College of Medical Genetics and Genomics 'ACMG' guidelines  [28, 29]. Search for large deletions/duplications of the CDH1 gene using multiplex ligation dependent probe ampli cation (MLPA) assay A total of 28 gDNAs and 10 sDNA were screened for copy number variations (CNV) using Multiplex Ligation Dependent Probe Ampli cation (MLPA).
The assay was performed using the SALSA P083-D2 CDH1 MLPA kit (MRC-Holland) according to the manufacturer's instructions. The kit contains 35 probes with ampli cation products ranging from 130 to 140 nucleotides. These include 20 probes for the CDH1 region, one upstream anking probe and one downstream anking probe. Samples containing 50 ng DNA were analyzed by MLPA using probe mix P002. Female control DNA was obtained from Promega. Blood DNA from an individual known to have a deletion of exon 11 of the CDH1 gene was used as a positive control. MLPA products were run on the ABI Prism 3730 xl Genetic Analyzer (Applied Biosystems) and analyzed using the Peak Scanner™ software v1. 0 (Applied Biosystems).
biocompute. org. uk/) to predict the functional consequences of coding variants (Non-Synonymous single nucleotide variants) and noncoding variants and Varsome (https://varsome. com/), which is a variant knowledge community, data aggregator and variant data discovery tool. All identi ed variants were classi ed based on their pathogenicity (benign, likely benign, pathogenic or likely pathogenic). All rare and novel variants were cross-referenced with general mutation databases (ClinVar) (https://www. ncbi. nlm. nih. gov/clinvar/), Leiden Open Variant Database (LOVD) (https://www. lovd. nl/) and UniProt as well as published reports to prioritize them for processing workup. In silico analysis method of c. 2281G>A mutation effect.
Preparing the structures The structure of Cadherin-1 (E-Cadherin) has been partially solved. To date, there are 14 available crystal and Cryo-EM structures from the Protein Data Bank (PDB), from which we selected the one containing the mutation. We used MODELLER  [30]  to generate the structure of the mutant. To investigate the functional effects of the mutation, different protocols were applied. The FlexPepDock method from the ROSETTA package was used to re ne peptide-protein complexes. The protocol retains 300 structures of the low and high-resolution stages before calculating the energy score. In addition we used MODPEP  [31]  to generate an ensemble of conformations that are likely to bind the target for the wild-type and mutant forms. Within the MODPREP work ow, psipred was applied to assign the secondary structure, whereupon the structure of the peptide wasassembled using experimentally collected data.
Inthe nal stage, molecular dynamics was applied to re ne the structures. The ensemble consists of 1000 conformations, which are then processed for analysis. Finally, we run an in silico alanine scanning protocol from ROSETTA  [32]  to calculate the variation in the binding energy (DDG) between two partners after mutating each residue to Alanine. Data from the in silico study were analyzed using the MDTraj python library  [33].
CDH1 Genetic Testing A total of 34 Tunisian patients with DGC were selected for CDH1 germline mutations screening. In a rst step, a total of 27 CDH1 variants (Additional le 4) were identi ed and ltered using some exclusion criteria 1) Do not consider polymorphisms and synonymous variants 2) Exclude variants reported in Clinvar as Benign or Likely Benign. Of the 27 variants, two were novel (c. 765G>A and c. 1565+3_1565+4delinsGT) and 10 were classi ed as polymorphisms, because the Minor Allele Frequency (MAF) in the 1000Genomes database was greater than 1% (c. 48+6C>T, c. 531+10G>C, c. 1320+45G>C , c. 1566-80C>G, c. 1712-52G>C ,c. 1896C>T, c. 1937-13T>C , c. 2076T>C, c. 2164+17dupA, c. 2439+52 G>A and c. 2634C>T). Two out of 27 (7. 41%) variants, previously identi ed by Sanger Sequencing, were predicted to be deleterious by various in silico tools (Table  2 ).
The distribution of identi ed CDH1 variants is shown in Fig. 1. Exonic coding variants represented 10 out of 27 variants (37. 04%) and based on the ClinVar database, variants were classi ed as benign or likely benign (62. 96%), one was described as VUS (3. 7%) and two were novel variants (7. 4%). Indeed the splice site variant (intron 10) seems to have a deleterious effect. The VUS "c. 2281G>A" located at Exon 14 corresponds to the cytoplasmic domain of E-Cadherin.