dataesr/scientific-paragraphs-categorization huggingface dataset:
τ ¼ t s • N -1 ð Þ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi N N-2 þ t s 2 ð Þ p ð5Þ The dataset is available in Zenodo  (Duché et al. 2017 , https:// doi. org/10. 5281/zenodo. 162978). Associated metadata is available at https://agroenvgeo. data. inra. fr/geonetwork/srv/ eng/catalog. search#/metadata/184ff098-eae2-44e1-9b8b-2e25a456c9a6. Zenodo is an open international research data repository created by the CERN and OpenAire that allows to store and share research datasets and provides digital object identifier. The whole dataset consists of four tables: 1. The first table (LFMC_final_Table. csv) contains the live fuel moisture content (LFMC). These are the robust estimates of LFMC and their associated standard errors which were both estimated from raw data with the method described above.
Technical validation Figure  2  shows typical LFMC seasonal variations for two species at a given site and their 95% confidence limits (shaded Figure  2  shows that LFMC generally decreases along the drying periods and increases after rainfalls. Some exceptions exist (Figure  2 , Quercus coccifera after the rain event around Doy 210), suggesting that other processes, such as phenology or tissue ageing, might affect LFMC variations  (Jolly et al. 2014 ). As a basic analysis, we applied a linear model to predict the lowest annual values of LFMC (10th percentile) with different variables including (i) a linear effect of a drought index (rain/ETP from June to September) computed each year for each site with the French climate reanalysis SAFRAN (see Section 2), (ii) a regeneration strategy effect accounting if a species is a seeder or resprouter (this information was taken from the Brot database  Paula et al.
2009 ) and (iii) a site effect. The global model leads to a R 2 of 0. 51. All parameters were significant (Table  2 ). The model coefficients are reported in supplementary S6 and indicate that (i) drought was associated with a decrease in lowest annual values of LFMC, that (ii) resprouters tend to have a higher minimum LFMC than seeders in agreement with physiological expectations  (Vilagrosa et al. 2014 ) and that (iii) unidentified features related to sites (e. g. soil, leaf area index) contribute to determining LFMC. A graphical illustration of the three effects is given in supplementary S6. The robustness of rainfall measurements of geolocalized sites was also evaluated with SAFRAN. We found a significant relationship between both datasets (supplementary S5). The rainfall and LMFC peaks generally co-occur (e.
Discussion and conclusion The dataset can be used for the following applications: & The study of the weather drivers of LFMC variations and its interactions with species and functional groups functioning (e. g. Viegas et al. 2001 ) & The calibration and validation of eco-physiological models of plant desiccation  (Ruffault et al. 2013; Martin-StPaul et al. 2017)  or fire behaviour  (Zylstra et al. 2016). & The calibration and validation of meteorological drought proxies of LFMC  (Viegas et al. 2001; Pellizzaro et al. 2007). & The calibration and validation of remote sensing products used to estimate LFMC  (Yebra et al. 2013; Fan et al. 2018 ) & The generation of dynamic fuel models  (Scott and Burgan 2005 ) & The analysis of relationship between LFMC and flammability of plants  (Fares et al.
2017 ) and wildfire activity  (Dennison and Moritz 2009; Chuvieco et al. 2009; Nolan et al. 2016)  Altogether, this database may help to improve live fuel moisture and fire danger modelling. If drought is a primary driver of LFMC, it also depends on phenology and tissue ageing  (Jolly et al. 2014 ), which should be accounted for in LFMC modelling. Also, the spatial variability of LFMC may partly be explained by local soil and vegetation properties. Ongoing field measurements carried out by our group aim at consolidating sites descriptions, with additional information regarding soil, vegetation cover and structure, as well as site history (fire occurrence or fuel cut during the measurement period), and will be released as soon as possible. Such data are critical to better understand and model LFMCdynamics.
2 ). Taken together, these results confirm that the single-cell datasets we have generated are of sufficient coverage, specificity and overall quality to unambiguously identify the profiled cells as contractile VSMCs. Regional differences in VSMC gene expression. We applied single-cell transcriptomics to gain insight into the previously observed VSMC heterogeneity between the athero-prone aortic arch (AA) and the more disease-resistant descending thoracic aorta (DT)  15, 16. AA and DT VSMCs differ in expression of a number of genes, including posterior Hox genes and other transcription factors  15. These differences may arise from the distinct embryonic origins of VSMCs in the AA (neural crest) and DT (mesoderm) regions  7. Alternatively, VSMCs in both regions could be heterogeneous with respect to these genes, with specific cell subsets represented in different proportions in the AA compared with the DT.
2b  and Supplementary Data 2b), consistent with the increased response of AA cells to mitogens and inflammatory cytokines  7, 17. Additionally, genes typically associated with the synthetic VSMC phenotype (Spp1, Pde1c)  3, 18  were expressed at higher levels in the AA compared with the DT VSMCs (Fig. 2b ). In summary, population-level analysis of VSMC region-specific transcription identified VSMCs (green, data from Gene Expression Omnibus accession GSE79436, Adhikari et al. 13  ) molecular determinants that may underlie the reported regional differences in VSMC growth factor response and disease susceptibility. Vascular region identity manifests in individual VSMCs. The scRNA-seq dataset described above (Fig. 1 ) comprised 79 AA and 64 DT cells, allowing comparison of the transcriptional signatures associated with VSMCs from these regions at a single-cell level.
This method ranked Rgs5 (also identified using the robust approach) and Vcam1 among the top HVGs in both AA and DT regions. Vcam1 is expressed in endothelial cells, but has also previously been detected in VSMCs  26  , where it is upregulated in response to proinflammatory cytokines  27. Notably, another variably expressed gene highlighted in the DM analysis encodes Stem Cell Antigen 1 (Ly6a/Sca1; ranked within the top 1. 2%). We next assessed the expression of the identified heterogeneously expressed genes in a much larger dataset (~2800 cells) of dissociated aortic cells generated using 10X Chromium technology, which has a higher throughput, but reduced coverage compared with the Fluidigm C1-based analysis presented above  28. As shown in Fig. 4d , t-SNE analysis of this dataset revealed clusters of arterial cells corresponding to VSMCs, endothelial and adventitial cell populations based on marker gene expression (including Myh11/Cnn1, Cdh5 and Pdgfra, respectively, Supplementary Fig.
Increased expression of VSMC response genes in Sca1+ VSMCs. To identify gene signatures underlying the observed heterogeneity of S+L+ cells, we initially employed the robust HVG method used above for the analysis of the AA and DT VSMC populations. We identified 52 genes showing highly variable expression within the S+L+ population (Supplementary Fig. 7a, b  and Supplementary Data 6), which fell into three network modules according to weighted gene co-expression network analysis (WGCNA, Fig. 7d  and S7c, d ). The module showing the highest level of co-expression consisted of 24 genes, including key contractile markers (Myh11, Acta2, Tagln and Cnn1; Fig. 7d ), and we will refer to it as the "cVSMC network". As expected, the cVSMC network was expressed highly in conventional S-L+ VSMCs, variably across S+L+ cells and at low levels in S+L-cells (Supplementary Fig.
7i  and Supplementary Data 9). We also found enrichment for genes associated with activation of many signalling pathways (PI3K, small GTPases, Tgf-beta, chemokines), suggesting that the cVSMCneg gene signature reflects a responsive cell state. Notably, a number of GO terms (including cell adhesion, migration and ERK1/2 signalling, Fig. 7h ) showed enrichment among both cVSMCpos and cVSMCneg genes. However, we noted that in these cases the positively and negatively correlated genes promote different functions within the same broad For example, inspection of the focal adhesion pathway annotated in KEGG database  40  suggested that cVSMCpos genes enhance stress fibre formation, while cVSMCneg genes promote cell proliferation (e. g. , Pak3, Supplementary Fig. 8a ). Similarly, in the MAPK pathway, genes that correlate positively with the cVSMC score are generally inhibitory, whereas negatively correlated genes promote growth factor signalling (Supplementary Fig.
8b ). Collectively, our findings demonstrate that S+L+ cells are a heterogeneous cell population showing progressively lower levels of contractile VSMC genes (cVSMCpos) and higher expression of cVSMCneg genes that are associated with responses to diverse signals. Sca1 expression is a hallmark of VSMC stimulation. To test whether Sca1 expression is linked to VSMC phenotypic modulation, we initially compared levels of Ly6a/Sca1 transcripts in VSMCs isolated from healthy tissue with cultured VSMCs, representing a partial model of phenotypic switching. Increased Ly6a/Sca1 expression in the cultured cells was evident by both RT-qPCR analysis (Fig. 8a ) and in scRNA-seq datasets (Fig. 8b ). The increased Sca1 expression in cultured VSMCs could result from selective expansion of pre-existing Sca1+ medial cells or upregulation of Ly6a/Sca1 transcription in contractile VSMCs upon phenotypic switching.
c PCA plot summarising the expression of the 500 most variable genes in S-L+, (magenta triangles), S+L+ (yellow squares) and S+L-(blue circles) cells. d The co-expressed cVSMC network module detected by weighted gene co-expression network analysis (WGCNA) based on 52 highly variable genes (HVGs) identified in S+L+ cells. Co-expression strength is indicated by edge thickness. e The PCA plot as in c, with cVSMC scores for S-L+ (triangles), S+L+ (squares) and S+L-(circles) cells colour-coded on a scale from blue to red. f Heatmap showing the expression of genes which positively (cVSMCpos, red side strip) or negatively (cVSMCneg, blue side strip) correlated with cVSMC score (likelihood ratio test, fdradjusted p-value < 0. 05). Rows represent genes and columns represent cells ordered by their cVSMC score (colour-coded from light to dark purple, lower strip).
g Boxplots showing the expression score of cVSMCpos (left) and cVSMCneg (right) genes for S-L+ (magenta), S+L+ (yellow) and S+L-(blue) cells. Expression scores are calculated based on PC1 as described in the Methods section. h Bubble plot of selected GO terms enriched in cVSMCpos (left) and cVSMCneg (right) genes. Dot size represents the number of genes overlapping with each GO term and the adjusted p-value is colour-coded from red to blue. The full list of enriched GO terms is given in Supplementary Data 9. i Dot plots showing the normalised expression of cVSMCpos gene Myh11 and cVSMCneg genes Mgp, Col8a1, Igf1, Pak3 and Vcam1 across S+L+ cells, ordered by cVSMC score. Boxplots show median (centre line), first and third quartiles (bounds of box) and 1. 5 interquartile range (whiskers) and data for individual cells (dots) in healthy animals, and yet find that VSMCs uniformly show hallmarks of their vascular regional identity.
a t-SNE visualisation of 10X Chromium dataset (3314 cells) of lineage-positive cells isolated from aortic plaques and the underlying media in Myh11-CreERt2/Confetti mice that were first tamoxifen-treated and then fed a cholesterol-rich diet for 14 or 18 weeks (tissue from three and two animals respectively was pooled for the analysis). The cells are grouped into a large population composed of 9 clusters, including one enriched for chondrocytic genes (cluster 8, magenta), and a smaller, distinct population, which was enriched for macrophage genes (cluster 9, pink). b t-SNE plot of 10X Chromium dataset from a with Ly6a/Sca1 expression colourcoded from light to dark grey. c, d t-SNE plot of 10X Chromium from a, with expression scores for cVSMCpos (c) or cVSMCneg (d) genes colour-coded on a blue to red gradient.
The dissociated cells were washed once in FACS buffer (1% BSA in PBS), incubated with FcX (1:100, Biolegend) to block FcRIII binding, stained with APC-conjugated anti-Sca1 (1:10, Miltenyi 130-120-343) or isotype control antibody (1:10, Miltenyi 130-102-655) and filtered through a 40 μm cell strainer. Stained cells were analysed on an Ariafusion (BD Bioscience) and FlowJo V10 software was used for data analysis and quantification. Analysis of aortic cryosections. After tissue dissection, aortas were fixed in 4% paraformaldehyde in PBS for 20 min, followed by overnight cryoprotection in 30% sucrose in PBS, 1 h incubation in a solution of 50% TissueTek O. C. T in 30% sucrose in PBS, 1 h incubation in 100% O. C. T and then snap-frozen in O. C. T. Serial transverse cryosections (12 µm) were then cut onto Superfrost Plus microscope slides, washed in PBS, stained with DAPI at 1 µg/ml in PBS and mounted in RapiClear 1.
No difference in recombination frequency or detection of Confetti-positive cells in the endothelial and adventitial layers were detected between the two chase timings. Imaging and image analysis. Live cultured cells were imaged using an Olympus IX71 fluorescence microscope with HCImage Live software (Fig. 8c ). Confocal imaging was done using a Leica SP8 with LASX software and a 40x objective (Figs. 5b, 6b, c, 7a, 9f, Supplementary Fig. 6b  and Supplementary Fig. 10 ) or Zeiss 700 with Zeiss Zen software and a 20x objective (images used for quantification in Fig. 8d ). Cell counting and image processing was performed using ImageJ or Imaris software. Staining intensities in Fig. 5c  were calculated from maximum projections of 14-24 1 µm z-stacks, representative images are shown in Fig. 5b. Bulk gene expression analysis.
5 µg RNA using the Quantitect® RT kit (Qiagen) according to the manufacturer's instructions. qPCR was performed using SYBR Green PCR master mix (Applied Biosystems, Life Technologies) and a Rotor-Gene cycler (Qiagen) in duplicate reactions. Primer sequences are provided in Supplementary Table  1. Quantification was performed relative to a standard curve generated from serial dilutions of ex vivo whole-aorta cDNA and normalised using the weighted average of housekeeping genes Hprt1 and Yhwaz or Hmbs. Analysis of bulk RNA-seq data. Reads were aligned to the GRCm38 mouse genome using Tophat 53 v2. 1 and read alignments per gene were counted using Seqmonk v1. 42 (http://www. bioinformatics. babraham. ac. uk/projects/seqmonk). Differential gene expression analysis was performed with Bioconductor R package DESeq2 54 v1.
Single-cell transcriptomes generated using the Smart-seq2 protocol were quality-controlled based on the total number of reads (more than 100,000) and genes (more than 1500) detected and the percentage of reads mapping to ERCC controls (below 30%). Gene-level read counts were normalised using the computeSumFactors function from the Bioconductor R package scran 22 (v1. 2 for Fluidigm C1 data and v1. 8 for Smart-seq2 data). For joint PCA analysis of singlecell and control samples (tube controls), estimateSizeFactorsForMatrix function from DESeq2 v1. 14 (locfunc = shorth) was used for normalisation. Random forest analysis. Cells were split into training (108 cells; 75%) and test (35 cells; 25%) sets. Recursive feature elimination with 10-fold cross-validation was performed on the training set using the caret R package (v6.
0). This procedure identified the 30 most relevant genes, which were then used as features for training a random forest classifier (number of trees = 1000) with 10 times repeated 10-fold cross-validation using the randomForest R package v4. 6. The R package ROCR v1. 0 was used to generate the ROC plot (Fig. 3d ). Detection of highly variable genes (HVGs). HVG analysis was performed separately on AA, DT and S+L+ cells. We used a strategy based on variance decomposition of log-transformed normalised expression counts into technical variance (learned from data as a function of log-transformed mean count for the AA and DT cells and estimated using ERCC spike-in controls for S+L+ cells) and the biological variance of interest, whereby HVGs are defined as genes with the biological component of variance significantly greater than zero.
The analysis was performed using the trendVar and decomposeVar functions in the Bioconductor R package scran  22  (v1. 2 for Fluidigm C1 data and v1. 8 for Smart-seq2 data). To obtain robust estimates, we repeated the analysis 1000 times using a 90% subset of cells for each run. The p-values from the one-tailed HVG tests for each run were non-independent and so could not be combined using standard methods. We therefore adopted an approach based on work by Licht and Rubin 49 , whereby the p-values for each gene g and run i are transformed into z-scores z g,i using the quantile function of the standard normal distribution. Gene-level z-scores are then computed as z g ¼ z g;i T g;i , where z g;i is the mean z-score and T g,i is the total variance of z-scores, respectively, across m = 1000 runs for a given gene.
The total variance T g,i is taken to be: T g;i ¼ VarðZ i Þ þ VarðZ g;i Þ; ð1Þ and estimated as: T g;i ¼ 1 þ 1 m À 1 X m i ðz g;i À z g;i Þ 2 ; ð2Þ given that Z i ∼ N(0,1). The corresponding gene-level p-values are then obtained from the probability density function of the standard normal distribution and adjusted for multiple testing using the Benjamini-Hochberg procedure. Genes with adjusted p-values below 0. 05 were considered HVGs. Notably, this approach produced very similar results to Hartung's method for combining dependent pvalues  58  (as implemented in R package punitroots 59 v0. 0-2), but yielded more conservative estimates for genes showing a large variance of HVG test p-values observed across runs. We also ranked genes based on the difference between the squared coefficient of variation (SCV) of their expression values and the median SCV expected given their sample mean, using an approach proposed by Kolodziejczyk et al  25.
and implemented in the function DM in scran 22 v1. 6. Annotation of HVGs. The functional annotation of the 147 HVGs identified using the variance decomposition-based method in at least one vascular region was performed based on NCBI PubMed citations listed under the entry for the Mus musculus reference sequence for each HVG in the NCBI Gene database (https:// www. ncbi. nlm. nih. gov). The identified citations were manually examined for experimental evidence of direct regulation of SMC function, cell proliferation, cell migration, inflammation, stem cell properties and/or development of cardiovascular disease (Supplementary Data 4c). Co-expression analysis and combined gene expression scores. To define coexpressed modules among HVGs detected in S+L+ cells, we used weighted correlation network analysis implemented in the R package WGCNA  60, 61  v1.
63. Specifically, we used blockwiseModules function with the following parameters: power = 3, TOMType = "unsigned", minModuleSize = 5, reassignThreshold = 0, mergeCutHeight = 0. 25, with the soft-thresholding power chosen based on assessing the dependence between it, the scale-free network topology and mean connectivity based on plots produced by pickSoftThreshold. The identified modules, in particularly the core VSMC network (ME1) used for expression correlation analysis, were robust to parameter variation, such as using different soft-thresholding powers and signed instead of unsigned correlation networks. Network modules in Fig. 7d  and Supplementary Fig. 7c, d  were visualised in Cytoscape 62 v2. 6. 1, with edge thickness proportionate to WGCNA co-expression weight between respective gene pairs.
Expression of network modules and other gene sets presented in the paper was summarised by their first principal component defined across analysed cells. Summarised expression of ME1 is referred to as the cVSMC score. Since the signs of principal components are arbitrary, we additionally set the sign of summarised expression values such that they positively correlate with the mean expression values of the respective gene sets across cells. Detection of genes correlated with the cVSMC network. To detect genes whose expression correlate (positively or negatively) with the cell's cVSMC network score (summarised expression of ME1, see above), we employed an approach taken for trajectory association analysis in the Bioconductor R package monocle  63  , with some modifications. First, we estimated the mean-variance relationship based on all genes by fitting a robust local regression to the log-mean normalised counts versus log-squared coefficient of variation (log-SCV) using the loess function in R (family = "symmetric").
The dependence of normalised expression counts for a given gene and cell on the cell's distance along the PC1 axis d t was then assessed by negative binomial (NB) regression implemented in the R package VGAM 64 v1. 0-5, whereby the size parameter of the corresponding NB distribution was assumed to be the inverse fitted SCV obtained as described above. The significance of the regression fit was assessed by the likelihood-ratio test versus the intercept-only model and the resulting p-values were adjusted for multiple testing using the Benjamini-Hochberg procedure. Genes showing adjusted p-values below 0. 05 were selected for further analysis. Analysis of the 10X Chromium single-cell RNA-seq data. Raw sequencing reads were processed and aligned to the GRCm38 mouse genome through the 10X Genomics cellranger pipeline (v2.
0 for VSMC-derived plaque cells and v2. 1 for whole aorta cells and VSMC-lineage cells from healthy vessels). Quality control was performed based on total number of UMI counts per cell (atherosclerotic plaque dataset >5000 and <20,000, whole aorta/lineage-positive medial cells >1000 and <8000 UMI counts), genes detected per cell (atherosclerotic plaque dataset >1000 and <5000, whole aorta/lineage-positive medial cells >500 and <2500) and percentage of mitochondrial reads per cell (atherosclerotic plaque dataset <9%, whole aorta/lineage-positive medial cells <8%). Reads from cells passing the quality control criteria were normalised and scaled using the NormalizeData (scale. factor = 10,000) and ScaleData functions from the CRAN R package Seurat 65 v2. 3. 1. Principal component analysis was performed and the first five principal components used for clustering and t-distributed Stochastic Neighbour Embedding (t-SNE)  66  visualisation using the Seurat package.
Differential gene expression between clusters was determined using the "tobit" method of the FindMarkers function implemented in Seurat (p adj < 0. 05). For identification of genes upregulated in individual VSMC clusters in the analysis of cells from the whole aorta, minimum log fold change of 0. 25 and minimum percentage of expressing cells of 25% were required for identification as an upregulated gene. Gene ontology analysis. Gene ontology (GO) analysis of genes differentially expressed between AA and DT cells was carried out using the online Functional Annotation Clustering tool in The Database for Annotation, Visualisation and Integrated Discovery (DAVID 67 v6. 8) for all Biological Processes (BP) terms against all Mus musculus genes with medium classification stringency. Clusters were ranked by enrichment and fdr-adjusted p-values for the most significant term listed.