dataesr/scientific-paragraphs-categorization huggingface dataset:
Together, these three datasets document a broad span of VSMC types and phenotypes contributing to vessel wall remodelling. The transcriptome analysis used GENCODE annotation as a reference and included a transcript assembly step to further identify transcripts not previously described in GEN-CODE (newly assembled transcripts). This approach allows the identification of novel isoforms for GENCODE genes, but also allows the identification of novel genes (newly assembled genes). This analysis was carried out independently for the three datasets. These expanded transcriptomes consisted of ~80,000-90,000 transcripts in total with 0. 6-1. 5% transcribed from newly assembled genes (Table  S1 ). To identify high confidence lncR-NAs from these complete transcriptomes, we used "Pipeline for Annotation of LncRNAs" (PLAR)  [23] , which filters lowly-expressed or artefactual transcripts and assesses coding potential based on three distinct tools.
As expected, the bulk of expressed transcripts were annotated as protein coding (Figure  S1A ). However, a high confidence set of ~2500-3000 lncRNA annotations were predicted within each transcriptome, with 6-7% deriving from newly assembled gene loci (Table  S1 ). Our analysis of robustly expressed genes showed that newly assembled lncRNA genes produced transcripts with comparable lengths to transcripts from GENCODE protein-coding genes (PCGs) or GENCODE lncRNA genes (Figure  S1B-E ). Of these genes, newly assembled and GENCODE lncRNAs have a lower expression compared to PCGs, as expected. Further, the GENCODE lncRNA genes were also more abundant than the newly assembled lncRNA genes but only by a median difference of ~1 FPKM (Figure  S1F-I ). To show the validity of the transcript discovery pipeline for lncRNA identification, we assessed if the newly assembled lncRNA transcripts identified in the three datasets were observed in other reference databases.
Using GFFcompare  [24] , we cross-referenced expressed GENCODE and newly assembled lncRNA transcript structures to transcripts annotated in FANTOM CAT  [15] , a particularly extensive reference annotation. We observed 72% of GENCODE and 40% of newly assembled lncRNAs matched to a FANTOM transcript containing the exact same chain of introns whilst another 25% of GENCODE and 40% of newly assembled lncRNAs contained at least 1 matching splice junction site (Figure  1b ). The validation of the complete or partial gene structures for a large proportion of the newly assembled lncRNAs in other annotation sets (derived from other contexts) provide confidence in the identified transcripts and evidence of the lncRNA expression in different datasets. To find further corroborating evidence of transcription for our lncRNAs, we used FANTOM CAT CAGEseq data which accurately defines transcription start sites (TSSs) in ~1800 distinct human samples through sequencing the site of RNA 5 capping  [15].
We identified 74. 3% of newly assembled and 86. 9% of GENCODE lncRNA genes across all datasets matched to experimentally validated TSSs in FANTOM CAGEseq data (hereafter referred as CAGE-matched lncRNAs) (Figure  1c ). The position of these CAGEseq matches indicates the first exons of newly assembled lncRNAs from our analysed datasets were largely complete at their 5 ends (incomplete by median of 8% of their initial size) (Figure  S2 ). To gain perspective on the in vivo relevance of newly assembled lncRNAs, we assayed their expression in an RNAseq dataset of carotid plaque tissue. VSMCs are major components of atherosclerosis plaque, with many demonstrating phenotypic modulation  [25]. To assess all newly assembled lncRNAs simultaneously, we merged the three expanded annotations into a non-redundant transcriptome containing 255 newly assembled lncRNA transcripts from 207 lncRNA genes (Figure  1d ).
Analysis of the plaque-derived RNAseq with this non-redundant merged transcriptome demonstrated that 50 (24%) of the newly assembled lncRNA genes were detectable in whole plaque tissue. This is a substantial detection rate, considering that plaques are heterogenous and contain non-VSMC cells contributing to the RNAseq. In addition, the newly assembled lncRNAs were identified in VSMCs from different vessel types grown in distinct conditions and so might not be expressed in plaques. Interestingly, these 50 lncRNAs come from all four independent transcriptomes (Figure  1e ), showing each new annotation provides in vivo relevant transcripts. Notably, 24 of these lncRNAs were identified exclusively using the svSMC dataset showing this annotation particularly improved coverage of plaque-expressed lncRNAs.
Newly Assembled Genes Substantially Increase the Number of VSMC lncRNAs Detected in Response to Pathological Stimuli With confidence in our expanded lncRNA annotation, we next sought to comprehensively identify all lncRNAs differentially expressed between conditions in the RNAseq datasets using DESeq2  [26]. For the svSMC dataset, we identified 162 differentially expressed lncRNAs between control and IL-1α/PDGF-BB stimulation (absolute fold change > 1. 5, p < 0. 05 *) out of the 598 robustly expressed lncRNA genes. Notably, newly assembled genes represented 32% of differentially expressed lncRNAs, more than would be expected by chance considering their proportion of total expressed lncRNAs (18%, p < 0. 0001 ****, Fisher's exact test) (Figure  2a ). Differential expression dynamics were validated via qRT-PCR in the same svSMC proliferation model for 7 GENCODE and three newly assembled lncRNAs chosen from the top 10% lncRNAs with the highest fold changes (Table  S2  and S3 ).
We obtained a high and significant correlation between the qRT-PCR and RNA-seq fold changes (R = 0. 97, p < 0. 0001 ****) (Figure  2b ). In the VSMC response to stiff culturing dataset, we identified 143 out of 551 and 168 out of 539 differentially expressed lncRNAs for aoSMC and caSMC, respectively. Again newly assembled lncRNAs made up a larger proportion of the differentially expressed. lncRNAs then would be expected by chance, considering their proportion of total expressed lncRNAs (Figure  2c, d ) (25% vs. 15%, p < 0. 001 *** and 21% vs. 15%, p < 0. 01 **, Fisher's exact test for aoSMC and caSMC, respectively). This indicates a particular tendency for newly assembled lncRNA genes to respond to IL-1α/PDGF-BB or increased vascular stiffness, both pathologically associated as pro-mitogenic stimuli.
For the plaque-isolated VSMC dataset, though a comparable portion of expressed lncR-NAs were newly assembled (16%), only 4 GENCODE lncRNAs and no newly assembled lncRNA genes were found differentially expressed between VSMCs from symptomatic and asymptomatic plaques. This small number of lncRNAs reflects the smaller number of transcriptional changes in between this dataset, even for protein coding genes (PCGs) (<1% of expressed genes) (Table  S4 ), likely explained by the higher heterogeneity of plaque-derived VSMCs compared to cultured VSMCs. To gain perspective on the role the differentially expressed lncRNAs may play in determining VSMC phenotypic state, we hierarchically clustered all differentially expressed genes based on their expression profile across all four conditions in either the saphenous vein-based model (Figure  2f ) or the ao/caSMC stiff culture-based model (Figure  2g ).
We identified six clusters of gene expression changes in each dataset and used gene ontology analysis (using goseq  [27] ) to associate them with biological processes, cellular components and molecular functions (Figure  2h ,i) (Tables  S5-S16 ). GENCODE and newly assembled lncRNAs were found in most clusters, suggesting the contribution of distinct lncRNAs across the different identified processes. We noted a large proportion of newly assembled lncRNAs in cluster 3 and 4 of the svSMC dataset, involved in cytokine and immune response, and in cluster 6 of the ao/caSMC dataset, involved in ribosome, Cajal body and mitochondrial activity. This indicates that newly assembled lncRNAs may be particularly relevant to these specific processes or compartments. Together our differential expression analysis showed that newly assembled lncR-NAs were more likely to be stimuli-responsive than reference lncRNAs, highlighting the importance of novel transcript discovery in pathological contexts.
We next aimed to identify VSMC-enriched lncRNAs. FANTOM CAT has previously defined genes with cell-type enriched expression as those with a 5-fold or greater enriched expression in a given category when compared to all other categories  [15]. Therefore, by selecting CAGE-matched lncRNAs with enriched expression in at least one VSMC type in the FANTOM CAT library, we were able to identify 72 VSMC-enriched lncRNAs across all datasets. Amongst the CAGE-matched lncRNAs, significantly more newly assembled lncRNAs were found to be VSMC-enriched than would be expected by chance considering their proportion of total expressed lncRNAs in svSMC (31% of VSMC-enriched vs. 12% of expressed lncRNAs, p < 0. 001 ***, Fisher's exact test), caSMC (31% of VSMCenriched vs. 10% of expressed lncRNAs, p < 0. 001 ***, Fisher's exact test) or plaque SMCs (20% of VSMC-enriched vs.
10% of expressed lncRNAs, p = 0. 03 *, Fisher's exact test) (Figure  3a, c, d ). This trend was maintained albeit with borderline significance for aoSMCs (20% vs. 10%, p = 0. 07, Fisher's exact test) (Figure  3b ). Overall, this demonstrates that the newly assembled lncRNAs have a greater tendency for VSMC-enriched expression when compared to GENCODE lncRNAs. To reveal the contribution of VSMC-enriched lncRNAs to VSMC phenotypic modulation, we evaluated their likelihood of differential expression in response to pathological stimuli. In the svSMC dataset, we found an increased tendency for VSMC-enriched lncRNAs to be responsive to IL-1α/PDGF-BB as compared to other lncRNAs (18% of differentially expressed vs. 8% of expressed lncRNAs, p < 0. 001 ***, Fisher's exact test) (Figure  3e ). This effect was also observed if considering lncRNAs responsive to stiffness in aoSMC (10% of differentially expressed vs.
6% of expressed lncRNAs, p = 0. 03, Fisher's exact test) and caSMC (13% of differentially expressed vs. 8% of expressed lncRNAs, p = 0. 02, Fisher's exact test) (Figure  3e-g ), but not in the plaque VSMC dataset (as expected due to the low number of differentially expressed genes). VSMC-enriched lncRNAs are therefore particularly likely to be involved in regulating VSMC response to physiological stimuli. In total, we identified 37 VSMC-enriched lncRNAs, including 17 newly enriched lncR-NAs, responding to either IL-1α/PDGF-BB or stiff-culturing (Figure  3i ). The differential expression of these lncRNAs appeared mostly exclusive to a specific dataset with only four lncRNAs upregulated by both IL-1α/PDGF-BB in svSMC and stiff culturing in aoSMC or caSMC. This suggests that the VSMC-enriched lncRNAs respond to different stimuli or are expressed and regulated in specific VSMC subtypes.
Novel Transcript Discovery in VSMCs Increases Evidence of Enhancer-Transcribed lncRNAs As non-reference lncRNAs have previously shown a particular association with enhancers  [18]  and lncRNA-producing enhancers have shown greater signs of activity influencing neighbouring PCG expression  [20] , we aimed to identify elncRNAs and their potentially regulated PCGs. To identify elncRNAs, we selected lncRNAs with a 5 region overlapping a Gene-Hancer  [29]  enhancer site or matched to a CAGE site previously classed as an "elncRNA" in FANTOM CAT (Table  S18 ). We found 110, 90, and 87 expressed elncRNAs in the svSMC, aoSMC or caSMC datasets, respectively. Newly assembled lncRNAs made up a higher proportion of the elncRNAs than would be expected by chance considering their proportion of all expressed lncRNAs (26% elncRNAs vs.
3 are both induced by IL-1α and PDGF-BB in svSMC and linked to expression of the cytokine IL-6 and the chemokine CXCL8 via GeneHancer interaction data (Figure  4j ,k). IL-6 and CXCL8 are key pro-inflammatory mediators known to be induced by IL-1α and characterised as promoting VSMC proliferation, migration, pro-inflammatory activity, and vascular remodelling  [30] [31] [32] [33] [34]. As mentioned above, VSMClnc6 has a high VSMC enrichment value, raising the prospect that this elncRNA may be part of a VSMC-enriched mechanism regulating the expression of CXCL8. 1 Newly assembled lncRNAs given name (see methods) along with their respective RNAseq dataset in brackets.
Discussion In this study, we used novel transcript discovery and a rigorous lncRNA prediction pipeline on two in vitro VSMC and one in vivo VSMC RNAseq datasets to expand the lncRNA annotation of VSMCs in pathological states. We identified 61-109 newly assembled lncRNAs expressed in each of these datasets. Interestingly, these newly assembled lncRNAs were more likely to be differentially expressed in response to pathological stimuli, to be VSMC-enriched, and/or transcribed from enhancer regions (elncRNAs), compared to previously annotated lncRNAs. As enhancers regulate local transcription, we also predicted neighbouring PCGs regulated by the elncRNAs and show many of these PCGs are involved in VSMC pathology. Taken together, we demonstrate that inclusion of nonreference transcripts is crucial to get a complete representation of lncRNAs and their contribution to the regulation of VSMC pathology.
VSMCs in such states are rarely used to annotate non-reference transcripts and so our expanded repertoire is a unique source of potential regulatory mechanisms that could contribute to many vascular pathologies. Both in vitro datasets analysed in this study model increased VSMC proliferation  [10, 21]  and this was confirmed in our analysis through identification of clusters of upregulated genes associated with cell-cycle processes. The presence of lncRNAs in these clusters shows their potential role in regulating proliferation. We also predict that two elncRNAs within the IL-1α/PDGF-BB induced cell-cycle cluster, AC002480. 3 and VSMClnc6, regulate the cytokine IL-6 and chemokine CXCL8 genes respectively. These two factors are known to promote VSMC proliferation  [31, 33] , yet are also indicative of the pro-inflammatory activity induced by IL-1α in the svSMC dataset.
This could be explained by a low representation of these pathways in GENCODE. If so, many newly assembled lncRNAs may represent stimulus-specific lncRNAs, a trait that could in turn explain why newly assembled lncR-NAs generally showed a high tendency to be differentially expressed. Further studies are required to study these stimuli-induced lncRNAs in non-VSMCs to determine if their expression is activated by the same stimulus on other cell types. FANTOM and GeneHancer databases were used in this study to validate the structure of the identified lncRNAs and provide further characterisation in terms of expression or location relative to enhancer regions. Further analyses would benefit from using datasets matching to the VSMC type/stimuli in the datasets rather than these generic databases.
For instance, no CAGE sites were found in FANTOM data for 26% of newly assembled lncRNAs. However, some of these missing TSSs could be identified using CAGEseq from VSMCs in the same context and these may represent particularly specifically expressed lncRNAs. Similarly, the identification of elncRNAs and their paired PCGs could be improved by studying chromatin marks in the same VSMC context as the RNAseq and by applying recently-developed, to link promoters and enhancers  [44]. The tendencies identified amongst newly assembled genes for differential expression, cell-specificity and enhancer association are in broad agreement with other human lncRNA annotation efforts describing skin psoriasis  [18]  and cell state transitions  [16, 17]. We underscore the value of using such pipelines to highlight areas not yet covered by reference annotation, even in the extensively annotated human genome.
RNAseq is an unbiased approach in comparison to microarray technology yet is under-utilised if relying solely on predefined reference annotation. An ever-increasing amount of high-quality sequencing data is available to profile lncRNA expression in a similar manner. Reference annotation cannot capture the full transcriptional variety of all cellular states at all times. Therefore, focused annotation efforts such as in this study allow capturing missing details. We reinforce that these missing details provide important definition by identifying key components of the lncRNAs associated with VSMC pathology. We demonstrate an easily implemented approach to achieve this and provide a resource to help identify key candidate regulators of VSMC pathological states for investigation. We recommend similar strategies to comprehensively map lncRNA and maximise knowledge of their function in homeostasis and disease more broadly, as well as their potential for therapeutic manipulation.
Transcriptome Assembly Sequencing files were obtained from gene expression omnibus (GEO) using accession numbers GSE69637 and GSE100081. We received files for a third dataset of plaque VSMCs produced by Alloza et al. [22]  by direct transfer from the authors. Data quality were checked via FastQC (version 0. 11. 9) [45]. Trimming of adaptor sequences was required for GSE69637 and done using TrimGalore (version 0. 5. 0) [46]. Custom transcriptomes consisting of GENCODE transcripts supplemented with newly assembled transcripts were generated as follows for each dataset. STAR (version 2. 5. 1b) [47]  was used to map reads to the human genome (GRCh38) indexed with GENCODEv26 (sjdbOverhang 100). StringTie (version 1. 3. 1c) [48]  was then used on these alignments to assemble transcripts (minimum length: 300 bp).
The StringTie assemblies were merged (using StringTiemerge) with a filtered reference set for each sample to create an expanded transcriptome. Newly assembled transcripts and genes were provided numeric identifiers with the prefix "MSTRG. ". The filtered reference set was obtained by removing transcripts with low expression (<0. 5 FPKM for spliced transcripts and < 1 FPKM for unspliced transcripts) and short transcripts (<300 bp). Transcript quantification was based on RSEM (version 1. 3. 0) [49]  (bowtie2).
Pipeline for Annotation of LncRNA (PLAR) + Classification of Transcripts RSEM  [49]  was used to quantify transcripts in each dataset using their corresponding expanded transcriptome. To annotate lncRNAs, we used the published pipeline PLAR  [23, 50]. For downstream analysis, we considered only expressed transcripts, defined as those with an average FPKM of at least 1 in an experimental condition. We also discarded minor isoforms for each newly assembled lncRNA gene, removing isoforms with an expression corresponding to less than 10% of the sum of all isoforms. Genes were classified based on PLAR transcript classification; genes producing any number of coding transcripts were labelled "coding", remaining genes producing putative lncRNAs (see above) were labelled "putative lncRNAs" and remaining genes producing high confidence lncRNA were labelled as "lncRNAs".
The CMS detector and event reconstruction The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3. 8 T. Within the solenoid volume are a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass and scintillator hadron calorimeter (HCAL), each composed of a barrel and two endcap sections. Forward calorimeters extend the pseudorapidity (η) coverage provided by the barrel and endcap detectors. Muons are detected in gas-ionization detectors embedded in the steel flux-return yoke outside the solenoid. The silicon tracker measures charged particles within the pseudorapidity range |η| < 2. 5. During the LHC running period when the data used in this paper were recorded, the silicon tracker consisted of 1856 silicon pixel and 15 148 silicon strip detector modules.
For each SR, five CRs are defined: dielectron and dimuon CRs enriched in Z → events, single-electron and single-muon CRs enriched in W → ν events, and a fifth CR enriched in γ + jets events. The SR events are selected using a trigger with a p miss T requirement of at least 120 GeV. The trigger requirement for the SRs is based on an online calculation of p miss T based on all PF candidates reconstructed at the HLT, except for muons. Events with high-p T muons are therefore also assigned large online p miss T , and the same trigger is used to collect data populating the singlemuon and dimuon CRs. The control samples with electrons are selected based on two different single-electron triggers requiring of p T > 35  (32)  GeV for 2017 (2018) and p T > 115 GeV, and on a single-photon trigger with a requirement of p T > 200 GeV.
CMS Figure  2 : Ratio of the dilepton to single-lepton control region yields predicted using simulation (red solid line), and observed in data (black points). The gray band represents the total uncertainty in the ratio. In the lower panels, the ratio of data over prediction is shown. From upper to lower, the rows show the monojet, low-purity, and high-purity mono-V categories, while the left (right) column represents the 2017 (2018) data set.
CMS Figure  3 : Ratio of the dilepton to photon control region yields predicted using simulation (red solid line), and observed in data (black points). The gray band represents the total uncertainty in the ratio. In the lower panels, the ratio of data over prediction is shown. From upper to lower, the rows show the monojet, low-purity, and high-purity mono-V categories, while the left (right) column represents the 2017 (2018) data set. bution of the lateral shower width of the photons. The distribution of this variable shows a characteristic peak for genuine photons, while being significantly more flat for the contribution from jets misreconstructed as photons. A template fit is performed to the distribution in data in order to extract the relative contributions of the two components. Templates for genuine photons are obtained from simulation, while templates for misreconstructed jets are taken from a CR in data with an inverted photon isolation requirement that is enriched in QCD multijet events.
Results and interpretation The ML fit is performed by combining the analysis categories as well as the 2017 and 2018 data sets. The p miss T distributions in the SRs before ("pre-fit") and after ("post-fit") the fit are shown in Fig. 4  for the monojet category and in Fig. 5  for the low-purity and high-purity mono-V categories. In all cases, good agreement is observed between the background-only post-fit result and the data. The corresponding distributions for the CRs are shown in Figs. 13-18 in Appendix A. 2. In the following, signal strength exclusion limits are presented for different signal hypotheses. Unless explicitly stated, all data sets and categories are included. The exclusion limits are calculated using the asymptotic approximation of the CL s method  [82] [83] [84]. In this method, a signalplus-background fit is performed for each signal hypothesis in addition to the background-only fit.
B. 5 Analysis implementation in MADANALYSIS The MADANALYSIS package is a framework for the reinterpretation of existing analyses in terms of arbitrary new physics models  [27]. The framework provides the infrastructure for the implementation of event selections that can be run over simulated signal events. Once an implementation is available, it is indexed in a public database that allows users to automatically download and execute it  [28]. In order to promote this analysis for reinterpretation, we implement the selection for the monojet category of this analysis in MADANALYSIS. A total of 66 analysis regions are defined, with each of the regions representing one recoil bin in one data taking year. The selections applied for the 2016 and 2017 data sets are identical, and additional criteria are applied to the 2018 data set, where mitigation requirements are used because of a localized problem in the hadron calorimeter.