dataesr/scientific-paragraphs-categorization huggingface dataset:
molgen. mpg. de/ CPDB] 90 that integrates different types of functional interactions from 30 public sources in order to assemble a more complete and a less biased picture of cellular biology. Currently, ConsensusPathDB contains metabolic and signaling reactions, physical protein interactions, genetic interactions, gene regulatory interactions and drug-target interactions in human, mouse, and yeast. We performed gene-set overexpression analyses using as a background the total list of autosomal genes covered by the Illumina EPIC array, a minimum set-size of 2 next-neighbors, and the following pathway databases: Reactome, KEGG, Wikipathways and Biocarta. For gene ontologies, we selected gene ontologies levels of 2 and 3 categories, and pathways, and both biological and molecular functions. Representations of enriched pathways were performed using cluster profiler R packages  91.
eQTL > 0. 05). (h) In SS patients, PLSCR1 expression increased with the dose of the minor A-allele of rs56077428 (ß SS. eQTL = 0. 357, P SS. eQTL = 0. 0208); however, in the healthy population it remains stable (P CTRL. eQTL > 0. 05). Green boxplots represent SS population, while grey boxplots represent the healthy control population. DNA methylation is quantified with β-values, gene expression is at the logarithmic scale. R software 73  and Adobe Illustrator (https:// www. adobe. com/) was used to create figures.
Acknowledgements Funding for the preparation of this manuscript has received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement nº  115 , 565 , resources composed of the financial contribution from the  European Union 's  Seventh Framework Program  ( FP7/2007-2013 ) and the  EFPIA companies ' in kind contribution. MT is supported by a Spanish grant from  Health Department, Junta de Andalucía  ( PI/0017/2016 ) and through the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  806975. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA. EC-M was funded by the  Postdoctoral Training Subprogramme Juan de la Cierva-Ministry of Economy and Competitiveness  ( FJCI_2014_20652 ).
Yellow line is a model that compared SSA + SS patients and CTRL. Blue line is a model that contrasted SSA + patients with CTRLs adjusted by SSB. Red bar is a model that contrasted SSA + SSB + patients and CTRLs. (c) Boxplots representing DNA methylation differences across different groups in three selected genes. R software 73  and Adobe Illustrator (https:// www. adobe. com/) was used to create figures.
Bioinformatic Processing and DE Analysis The complete analysis can be found at https://github. com/ lebf3/Dog_AF_transcriptomic. Briefly, mRNA reads were pseudomapped on reference transcriptome CanFam3. 1. 98 with Kallisto  16  with the options quant -t 5 -b 100 and the rest as default. We aggregated transcripts by genes with tximport  17  and quantified with DESeq2. 18 Genes with 0 reads in >12 samples were removed. Shrunken log 2 transformed expression corrected for library size, with and without fibroblast fraction as a covariate (within DEseq2's model) were then analyzed for DE with Wald test for all pairwise comparisons of CTL, AF, and AF+AVB and likelihood ratio test for total assay DE. We did not adjust our differential gene expression analyses for biological sex because no genes where DE between female and male dogs in our experiment.
miRNA Target Prediction For DE miRNA present in the 5 most cited miRNA databases (DIANA, Miranda, PicTar, TargetScan, and miRDB), we defined genes as targets if they were (1) annotated with a human homolog in the ensemble database, (2) predicted targets by at least 3 out of 5 databases queried with the MiRNAtap package, (3) DE (mRNA false discovery rate [FDR] <0. 01), (4) inversely correlated (Pearson r<-0. 5) log 2 expression, corrected for the fibroblast fraction (expression values corrected with Limma's removeBatchEffect() function). We then performed a gene set enrichment analyses (GSEA) with the remaining 82 predicted targets of the miRNA located on the syntenic region of the Dlk1-Dio3 locus (CanFam3. 1 Chr8:68961744-69696779) as described above.
RNA-Seq and miRNA-Seq DE Genes Comparison Between Human AF Patients and Canine AF Models DE genes in our canine AF models with annotated human orthologues in the ENSEMBL database were compared with a meta-analysis of miRNA DE in human AF and a large RNAseq study on left atrial appendages obtained from 261 patients undergoing valve surgery. 25, 26 Precursors of the human miR-NAs listed in Shen et al Table  S8  (n=53, 21 upregulated, and 32 downregulated)  25  were retrieved with the R package miR-BaseConverter and then compared across species. Because only 5 miRNA were found to overlap with human DE miRNA, only mRNA genes found to be DE in human and our canine AF models are represented as an Upset plot. The counts for human mRNA data were downloaded from GEO database (GSE69890). DE testing was conducted as described above for the 3 groups; no AF (CTL, n=50), AF in AF rhythm (AF, n=130), and AF in sinus rhythm (AF.
Proteomics Dog cardiomyocytes were lysed by sonication, reduced, and alkylated. Protein was precipitated, resuspended, quantified, and subjected to tryptic digest. Peptides (500 ng) were analyzed by reverse phase nano-HPLC coupled to a Bruker maXis II mass spectrometer (positive mode, mass range 150-2200 m/z, collision induced dissociation of top 20 precursors). LC-mass spectrometry/mass spectrometry data were analyzed for protein identification and label-free quantification using MaxQuant  28  (1. 6. 1. 0) against the public database UniProt with taxonomy Canis lupus familiaris and common contaminants (downloaded on 01. 08. 2019, 29809 sequences) with carbamidomethylation on Cys as fixed and oxidation on Met as variable modification with decoy database search included (mass tolerance 0. 006 Da for precursor, 80 ppm for product ions; 1% PSM and protein FDR, match between runs enabled, minimum of 2 ratio counts of quantified razor and unique peptides).
RNA-Sequencing of Cardiomyocyte-Enriched Atrial Samples From Canine AF Models We analyzed data from 3 groups of 6 dogs. The first group (CTL) was the control group without atrioventricular ablation (AVB) nor pacemaker, in the second group (AF), right atrial tachypacing at 600 beats per minute was used to maintain AF electrically for one week, and the third group (AF+AVB) included dogs with electrically maintained AF for one week in the presence of AVB and ventricular pacing at 80 beats per minute to control the ventricular rate. We reasoned that transcriptomic profiling of atria from these animals should allow us to discover the molecular changes that occur over the first week after the onset of AF and play a role in the development of the tissue remodeling accompanying the transition from paroxysmal to persistent AF.
Transcriptomic Changes in Cardiomyocyte-Enriched Atrial Samples Pairwise comparisons of gene expression levels between the three groups of dogs identified 434, 5971, and 7867 genes that are DE (FDR <0. 01) in atrial cardiomyocyterich fractions in atrial fibrillation versus control samples (AFvsCTL), atrial fibrillation+AV block versus control samples (AF+AVBvsCTL), and AFvsAF+AVB, respectively (Figure  3A   and https://github. com/lebf3/DogAF). Many genes previously implicated in AF are dysregulated in both AF and AF+AVB dogs when compared with controls, thus validating the experimental design. This includes FHL1 involved in myofilament regulation,  29  SORBS2 involved in intercalated disc gap junction regulation,  30  and KCNA5, which regulates atrial action potential repolarization. 31 revious studies have established an important role for mitochondrial dysfunction in the cause of AF.
27  Accordingly, we identified 54 genes that encode mitochondrial proteins that are DE in our AF canine models (Figure  I  in the Data Supplement). In particular in the AF+AVB group, we noted the upregulation of 2 key beta-oxidation genes (CPT1A and ACADL) and the downregulation of the electron transport chain genes COX17 and NDUFA8. However, our data also implicates genes not previously recognized to be involved in AF, such as leukocyte receptor cluster member-8 (LENG8), transcription elongation regulator-1 (TCERG1), ligand dependent nuclear receptor corepressor (LCOR), formin-binding protein-4 (FNBP4), and ENSCAFG00000049959 (orthologue of the lncRNA MEG3; Figure  3A , Table  III  in the Data Supplement, and https://github. com/lebf3/DogAF). To understand what pathways are modulated in the atria of these canine AF models, we performed GSEAs on the DE genes (Figure  3C  and Table IV in the Data Supplement).
For this analysis, we focused on DE miRNAs and DE mRNAs that are predicted to physically interact by at least 3 out of 5 databases and that have expression levels that are negatively correlated in the RNA-seq/ miRNA-seq experiments (Pearson r<-0. 5). Using these filters, we identified 82 potential target genes for the DE miRNAs at this locus, with most genes targeted by a single miRNA (Figure  4C ). GSEA with these 82 genes indicated a common role in synaptic signaling involving glutamate signaling (Figure  4D  and Table  V  in the Data Supplement). Some of the key genes within these pathways are metabotropic glutamate receptor-1 and -8 (GRM1, GRM8), glutamate ionotropic receptor delta type subunit-1 (GRID1), glutamate ionotropic receptor AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) type subunit-1 (GRIA1), and corticotropin-releasing factor-binding protein (CRHBP).
We found the strongest overlap between dog AF+AVB and human AF in AF rhythm. Of note, most of the strongest signals in our dog study are also present in this human study (LENG8, SORBS2, BMP10, FNBP4, and glutamate receptor-related genes [GRM1, GRM8, GRIA1, GRIK2, GRID1]). For miRNA, we compared our results with data from a large meta-analysis involving 40 articles and 283 DE miRNA in AF (in different tissues and species). 25 Of the 53 AF-associated miRNAs that were previously identified in human cardiac tissues and showed consistent results in the meta-analysis, we found 5 miRNAs in our analyses of the dog transcriptomic data sets (MIR144, MIR142, MIR146B, MIR223, and MIR451). These miRNAs have not been characterized functionally yet for a role in AF. Generally, the canine AF group matched better the directionality of change reported in the human AF DE miRNA meta-analysis.
DISCUSSION In this study, we used a transcriptomic approach to comprehensively assess the molecular architecture of AF-induced remodeling with and without AVB from atrial cardiomyocyte-enriched samples. We validated the robustness of our RNAseq data by correlating it with a proteomic analysis, which showed a strong correlation in gene expression, with well-defined cardiomyocyte genes being most highly expressed in both datasets (eg, TTN, MYL4). We confirmed the involvement of known AF factors like the reactivation of developmental pathways, but also found a strong and novel association with microR-NAs and lncRNA from the DLK1-DIO3 locus, including the MEG3 canine orthologue. This finding is concordant with the many chromatin remodeling genes dysregulated in our models, which is an emerging phenotype of AF both in human and sheep models.
We identified potential targets with the MiRNAtap package (predicted by ≥3 databases) from DE miRNA (FDR <0. 01) and DE mRNA (FDR <0. 01). D, Gene set enrichment analyses (GSEA) with the potential gene targets (x axis) of the DE miRNA located at the syntenic DLK1-DIO3 locus. We only present the top 5 pathways enriched in this analysis. A red square in the heatmap indicates membership of a given target gene to the biological pathways located on the left (empty columns were removed for clarity). GSEA FDR and AF+AVBvsCTL DE FDR are on the right and top of the heatmap, respectively. AF indicates atrial fibrillation; AF+AVBvsCTL, atrial fibrillation+AV block versus control samples; AFvsCTL, atrial fibrillation versus control samples. Downloaded from http://ahajournals. org by on April 13, 2022.
This picture changes dramatically if the focus is shifted from the relatively stable peer-to-peer network to the fluid information bipartite network, that is, ad hoc groups of users, which loosely gather around and engage in shared memes  21  , operating in a hyper-competitive environment  17. Other approaches, which did include the bipartite perspective, were limited to a qualitative discussion as a result of empirical observations on a single dataset  22  , failing to identify the mechanisms that drive the whole system. A picture that embraces the mentioned ingredients might open new promising possibilities to analyse and model online social networks if we consider that Ecology is rich in theoretical frameworks where the structure-dynamics coupling is studied  [23] [24] [25]. Moreover, while testing these theories empirically in natural ecosystems is difficult-mainly because of the resource-intensive demands to collect data  26  -digital streams from social interactions are abundant on several spatial and temporal scales, and precise knowledge about the environmental (external) conditions -related to specific information flows-can also be collected.
Results Structural flexibility in information systems. Biased as it may be  41  , Twitter is a sensitive platform that mirrors, practically without delay  [42] [43] [44]  , exogenous events occurring in offline environments. In this sense, Twitter data constitute a rich stream, providing a public and machine-readable reflection of the real world. Despite the highly fluctuating nature of this communication stream, some reliable patterns emerge from its activity. We analyse these streams in a longitudinal manner, as a series of snapshots from time-resolved activity. Each slice is represented as a bipartite network with a fixed number of the most active actors (users in this context; N U = 2000), and the corresponding memes (hashtags in this context; N H ) created and/or cited by these users, in that slice.
High values of nestedness, on the other hand, reflect an extraordinary (and, thus, ephemeral) stage in which the system self-organises to attend one or few topics. In these cases, the discussion revolves around a small set of generalist hashtags (hashtags used virtually by everybody) and users (highly active individuals participating in many facets of the discussion). Figure  1 , panels c and h, present the evolution of Q and N on two different portions of Twitter activity. For example, Fig. 1c  corresponds to a period of over 45 days around the local elections in Spain (April-May 2019). For this dataset, the evolution of Q and N shows a strongly anti-correlated behaviour. This behaviour can be explained by the mutual structural constraints that these two arrangements impose on each other, i.
The overall observed behaviour in Fig. 1c  is replicated across different types of events. Figure  1h  shows an equivalent pattern for a completely different event. In this case, the dataset comprises the reaction after the Nepal earthquake in 2015  51  , including a major aftershock on May 12th. Unlike a political debate or an election date, this example is inherently unexpected and unpredictable-an important fact, attending the taxonomy of collective attention described in Lehmann et al. 42. As in Fig. 1c , the coarse grain scale of days and weeks in Fig. 1h  is complemented with high-resolution monitoring of a portion of exceptional events (panel i). Four more datasets are analysed in Supplementary Note 1. In all of them, the previous observations hold: the anti-correlated evolution of Q and N (Supplementary Table  2 ), the alternation of modular and nested arrangements (Supplementary Fig.
In this altered environment, users temporarily engage with new kinds of hashtags, different from those they usually interact with (see "Methods" section and Supplementary Note 2. In an unperturbed simulated environment, the observed emergent structural arrangement mimics the prescribed organisation of niches in topical blocks. That is, a modular architecture arises from the random initial one, see Fig. 3e  and f, for t < 3 × 10 4 (note that the plot is shifted by Q 0 , i. e. , modularity at time 2 × 10 3 , once the system has stabilised its architecture). This is in line with the resting state observed in the datasets (Fig. 1e, f ), where users are focused on their own topics of interest. It is important to Here we show Twitter streams covering two different topics, i. e. , Spanish general election of 2019 (panels a-e) and the 2015 Nepal earthquake (panel f-i), respectively.
Spanning different time ranges and attracting varying levels of attention (see tweet volume in panels a and f), the information ecosystems self-organise in similar ways: a block organisation dominates the system (positive modularity Q), reflecting the separate interests of users until external events induce large-scale attention shifts, which rearrange completely the network connectivity towards a nested architecture (high N ), c and h. Note that, despite the predictable (Spain) vs. unpredictable (Nepal) nature of each stream, structural properties of the user-hashtag interaction networks ( Q with N ) are anti-correlated  22. For a closer view, we highlight specific time windows in each dataset with some identifiable events happening in them (panels d and e for the Spanish elections dataset and panel i for the Nepal earthquake).
On the other hand, when the network presents several blocks (B > 1), each one arranged in a nested manner, then I > N. It makes sense now to revisit the previous numerical and empirical results, now through the lens of in-block nestedness. Figure  1b  and g , and Fig. 3g  and h , respectively, monitor the relative size of the largest (N B 1 =N) and second-largest (N B 2 =N) nested blocks. In both empirical and numerical cases, we observe that nearly-perfect consensus is reached at different moments (N B 1 =N % 1), while a fragmented public sphere dominates most of the time. The relative size of the second-largest nested block (N B 2 =N) allows for an easier interpretation of the level of consensus reached at each time. Again, the analysis of additional datasets in Supplementary Note 1 confirms the generality of the results.
With language abuse, this is tantamount to the number of individuals (e. g. , hashtag instances) that build up the species (e. g. , the hashtag). Following this line of reasoning, for the empirical data on the hashtags' side, we can track the hashtag usage frequency over time, as a proxy for hashtag abundance from the model. We compare the evolution of such abundance in the model and the data (Spanish and Nepal datasets). Panel a of Fig. 4  shows, from our numerical simulations, the changes in abundances of the hashtags over time, identifying with a colour the topic they are   2 ). Panel e models the increase, sustainment and decay of attention in programmed events (e. g. , election day), represented as a monotonically decreasing yellow shade starting at t = 3 × 10 4. Panel f, instead, mimics the arrival of an unexpected exogenous event, represented also as a yellow shade.
Panel b of Fig. 4  shows the usage frequency of actual hashtags over time in the Spanish dataset, where events are known in advance (in this case, election day on April 28). Adapting the logic of the model to empirical data, we show the trajectories of a group of hashtags that belong to 4 different communities, the largest ones shortly before (light blue, orange, fuchsia) and at the time the ballots were closed (violet). The abundance of hashtags is represented here as their absolute frequency, shifted to the value at the beginning of the observation, i. e. , f h ¼ f h ðtÞ À f h ð0Þ, window to enable the comparison of their evolution. As in its model counterpart, the vertical lines show the buildup of conversations ahead of the results (around 4 p. m. , "event onset" tag), and the electoral schools closing time (8 p.
Hashtags in other topics (blue, orange, fuchsia) experiment a slow decline. The same happens for the Spanish election day (panel b), regarding the evolution of hashtag frequencies fh ¼ f h ðtÞ À f h ð0Þ, although admittedly with fluctuations. In this case, each colour corresponds to different communities, as detected from the networks maximising Q. Panels c and d correspond to synthetic and empirical unexpected events. Except for the abruptness in the increase of the purple hashtags in the Nepal dataset (much faster than its synthetic counterpart), the similarities are clear to the naked eye. Note that, in panels b and d, hashtag frequencies are shifted to their initial value. Remarkably, all four panels evidence that, at the microscale, a sufficiently strong perturbation impedes the system to recover the pre-event state, i.
Methods Empirical and synthetic data. For both synthetic and empirical cases, we represent a bipartite unweighted network as a N U × N H matrix A, where rows and columns refer to users and hashtags, respectively. Elements, therefore, represent links in the bipartite network, i. e. , if the element a uh has a value of 1, it represents that the user u produced the hashtag h at least once, otherwise a uh is set to 0. For each empirical dataset, we split the Twitter stream into chunks according to non-overlapping time windows with three hours of duration ω = 3h, containing the N U = 2000 most active unique users, while the number of hashtags is variable (depending on the amount produced by those N U users). In this way, for each snapshot, a rectangular binary presence-absence matrix is created.
Acknowledgements M. J. P. , A. S. -R. and J. B-H. acknowledge the support of the  Spanish MICINN  project  PGC2018-096999-A-I00. M. J. P. acknowledges as well the support of a doctoral grant from the  Universitat Oberta de Catalunya (UOC). S. S. thanks the support of  UNIPD  through ReACT Stars 2018 grant. S. M. and V. C. -S. acknowledge partial financial support from the  Agencia Estatal de Investigacion (AEI, Spain)  and  Fondo Europeo de Desarrollo Regional  under Project  PACSS  Project No. RTI2018-093732-B-C22  ( MCIU, AEI/ FEDER, UE ) and through the  María de Maeztu Program for units of Excellence in R&D  ( MDM-2017-0711 ). C. A. P. acknowledges the support provided by grant No. ANR-18- CE30-0013  from the  Agence Nationale de la Recherche. All authors acknowledge the support to the  TEAMS  project of the  Cariparo Visiting Program 2018 (Padova, Italy ).
Code availability The software code for nestedness measurement, and modularity and in-block nestedness optimisation, is available at https://github. com/COSIN3-UOC/N-Q-IBN with https://doi. org/10. 5281/zenodo. 4557009. The code for simulations of the dynamical model is available at https://github. com/COSIN3-UOC/dynamical-niche-model with https://doi. org/10. 5281/zenodo. 4555890.
At 48 hours post infection, whole blood was collected via cardiac puncture followed by hypotonic lysis of red blood cells and subsequent resuspension of remaining cells in PBS. Samples were placed on a glass slide, heat fixed and mounted in Cytoseal. Slides were imaged using a Leica TCS SPE Confocal microscope with a 63X objective, LAS X acquisition software, and processed using FIJI  (Schindelin et al. , 2012).
Selection of sgRNAs for each operon Primary operons (pTSS operons) were annotated in S. pneumoniae D39V strain in a previous study  (Slager et al. , 2018). First, for all the identified pTSS operons, one sgRNA with high specificity and close proximity to the pTSS was designed for each operon. For genes that are not covered by pTSS operons, one sgRNA was selected for each gene. S. pneumoniae has multiple types of repeat regions, such as BOX elements, Repeat Units of the Pneumococcus (RUP), SPRITEs and IS elements  (Slager et al. , 2018). There are some sgRNAs targeting genes located in repeat regions, and as such these sgRNAs have multiple targeting sites. The sgRNAs and targets are listed in supplementary table  S1. Post-hoc target identification, including off-target sites, was performed with a custom R script (https://github.