dataesr/scientific-paragraphs-categorization huggingface dataset:
Tree cover loss map from global forest watch To compare with the results obtained from the CuSum change detection algorithm applied to Sentinel-1 SAR images, the performances of the Global Forest Watch (GFW) Tree Cover Loss (TCL) map  (Hansen et al. , 2013)  were also evaluated against the visual interpretation of the PlanetScope images. Results obtained from this forest change cover analysis were subsequently used for assessing the performance of our detector. The TCL map is a dataset developed for monitoring forest cover loss worldwide based on Landsat-5 TM, Landsat 7 ETM+, Landsat 8 OLI/ TIRS and MODIS data with a pixel resolution of 30 m  (Potapov et al. , 2020). The TCL map over the study site is made freely available at https://data. globalforestwatch. org/ (Dataset: 'Granule 10N 20E' on GFW website).
Pre-processing The Sentinel-1 SAR images were pre-processed using VtWeb. VtWeb is an online platform enabling users to easily browse free Earth observation data, display it in 2D/3D and process it on-the-fly  (VtWeb, 2015)  accessible on https://visioterra. org/VtWeb/. The pre-processing performed by VtWeb to obtain geo-corrected backscatter images (resulting in γ 0 terrain corrected data) consisted in the 3 following steps (Fig. 3 ): -Orbit correction -thermal noise removal  (Piantanida and Miranda, 2017) , -terrain flattening as terrain correction to remove topological and incidence effects  (Small, 2011). The speckle was filtered using the bilateral filter made available in the python PyRAT Toolbox  (Reigber et al. , 2019)  with a kernel window size of 7 × 7. This filter is a spatial filter presenting the advantage of preserving edges in the images.
Spatial operations over the CuSum results The two maps resulting from application of the CuSum algorithm to the VV and VH SAR images were intersected to form the "VV intersect VH" dataset. In that case, a pixel is considered to be subject to a change when it is classified as 'change' at both the VV and VH polarizations. This dataset was made in order to assess if changes affect both volume and surface components. As the SAR backscatter is affected by the dominant scattering mechanism resulting from the signal/target interaction, the impact on polarisation channels can vary. A dominant surface or double-bounce scattering mechanism generally display a higher intensity on the VV channel, while a dominant volume scattering will greatly influence the VH backscatter signals. The changes affecting an area can modify these interactions, resulting in changes being more detected by a polarisation than by the other.
The maps based on the VV and VH results were joined to form the "VV union VH" dataset. In that case, a pixel is considered to be subject to a change when it is classified as "change" from at least one of the polarizations (either VV or VH). Our objective with these "intersection" and "union" maps were to check the consistency / difference of the results based on both the VV and VH polarizations and to evaluate their complementarity. High threshold values of T c (T c = 1. 00) were found to be the most robust values in terms of accuracy from preliminary tests. In order to reduce the number of false positives obtained from low T c values in the form of small clusters of change, a spatial recombination of T c thresholds was applied. Results obtained from high T c values were combined with those obtained from low T c values.
Statistics There are two types of errors when comparing two binary spatial datasets: i) the false positive, which corresponds to the reference map detecting no change while the algorithm detects a change, ii) the false negative, which occurs when the reference map detects a change whereas the algorithm does not detect it. In order to describe the quality of the matching between our S1-based retrieved change map and the reference PlanetScope change map, the following classical statistical indicators (Overall Accuracy as Accuracy, Recall, Precision, F-score and Kappa Coefficient) derived from the confusion matrix were used. Due to class imbalance between Change and No Change, the overall accuracy, recall and precision should be studied along with F-score and Kappa coefficient for interpretation.
Cross comparison against external datasets using confusion-matrix derived statistics When comparing results obtained for all mapping configurations based on VV, VH and their combinations (intersection and union) and for any T c value (0. 25, 0. 5, 0. 75, 1 and 100_25, 100_50 and 100_75), the precision ranges from very low (0. 21 for VV union VH for T c = 0. 25) to high (0. 93 for VV intersect VH for T c = 1. 00). Precision is very similar for all mapping configurations for T c = 1. 00 (0. 87 to 0. 90). The results of the different mapping configurations are very scattered for T c ranging from 0. 25 to 0. 75. The cross-Tc combination generally increases the precision (compared to the simple T c configuration), for instance the precision increased by 11. 0% for VH (compared to T c = 0. 5 against 100_50) or by 23.
54), recall (0. 57) and Kappa coefficient (0. 48). The Tree Cover Loss area estimated from GFW dataset was 204 ha (9. 0 % of the study area) vs 341. 5 ha (15. 1 % of the study area) for the PlanetScope-based reference map. The results of our method applied to Sentinel-1 SAR images has a higher Kappa coefficient than GFW in spite of being impacted by large false positives. Several clusters detected using the CuSum approach to the Sentinel-1 SAR images did not correspond to cuts based on the PlanetScope visual interpretation. As it can be seen in Fig. 9 , several zones were detected "as cover changes" by applying CuSum before the zones were    visually seen as deforested. The PlanetScope images exhibited a change in radiance in these zones but this change is much lower than that due to clear-cut.
59, Precision: 0. 77, Accuracy: 0. 91, Recall: 0. 55). The lack of in-situ data led us to use a PlanetScope-based reference map of the cover changes which is only based on remotely sensed optical observations and which present many limitations. In particular, degradation effects preceding deforestation were not monitored in this reference map, and canopy gaps can recover between two images. This could partially explain why our results, based on Sentinel-1 SAR images, exhibit more changes than detected in the reference map. This could also explain the lower scores obtained at VH than at VV when applying CuSum to Sentinel-1 images as observations at the VH polarisation are more sensitive to the degradation effects than at the VV polarisation  (Kellndorfer, 2019). This could affect the false positives detected using VH: they are more numerous compared to using VV.
Transcriptional characterization of vascular smooth muscle cells To identify regional-speci c transcriptional identities in vSMCs populations, we performed single cell RNA sequencing (scRNA-seq) in 3 distinct sites: carotid arteries, aortic arch (ascending thoracic) and descending (thoraco-abdominal) aorta (Figure  1a  and 1b ). The rationale for selecting these sites was the combination of embryonic origin, hemodynamics, and disease emergence. The aortic root was excluded from the evaluation. The collected aortic arch region extended until the left subclavian branch, in concordance with the limits of neural crest origin  5. In total, we sequenced between 3,732 to 4,632 cells per site with 72,685 to 91,597 average reads per cell (Supplementary Figure  1 ,2; Supplemental Table  2 ). Quality assessment of each of the three libraries was performed and those cells with low RNA counts and high levels of mitochondrial RNA were removed from the dataset prior to downstream analysis (Supplementary Figure  1 ,2).
Visualization of the data using t-Distributed Stochastic Neighbor Embedding (tSNE) highlighted the global uniqueness of each population of vSMC per anatomical region, as the data segregated into three distinct mostly contiguous but not overlapping groups that matched the referred anatomical location (Figure  2a , b, Supplementary Excel File 1). From this cell population 18,940 transcripts were quanti ed and assessed for site-enriched expression. In this analysis, 1045 transcripts (5. 5%) showed unique anatomic location-enriched expression (Figure  2c , Supplementary Excel File 1). Importantly, while vSMCs populations contained these regional-enriched expression patterns, their core identity as per expression of classical vSMC markers-Myh11, Acta2, and Tagln, were unchanged (Supplemental Figure  3 ).
Immuno uorescence identi cation of Perlecan (Hsgp2) revealed preferential expression in vSMC of the descencing aorta (Figure  5l  and 5m ). Identi cation of CD146/MCAM as a novel site-restricted aneurysm-associated gene The cohort of genes selectively expressed at speci c vascular locations (5. 5% of total) offered the opportunity to inquire about their potential contribution to regional-speci c pathologies. Thus, we performed an in-silico forward genetic screen of archived loci previously associated with mendelian vascular disease through integration of entries from the Human Phenotype Ontology (HPO) database (https://hpo. jax. org/app/)  22 with data from Online Mendelian Inheritance of Man (omim. org)  23. Following a detailed curation, 16 reported loci representing 6 vascular associated traits were identi ed (Figure  6a , Supplemental Figure  5  and Supplementary Table  5 ).
From each locus, a list of regional candidate genes was obtained using the UCSC table browser and subsequently overlaid with the siteenriched single cell data to identity novel candidate genes for each genomic region. A total of 13 transcripts predominantly expressed in the speci c vascular regions mapped to vascular disease loci (Supplementary Table  5 ). Given the discovery-driven nature of this approach, we sought to con rm the feasibility of validating putative disease candidate by further validating the regionally restricted expression of a disease-associated, site-enriched candidate, Tfap2b (Figure  6b , Supplementary Table  6 )  24. Immunohistochemistry at low magni cation revealed a clear predominance of TFAP2B in the ascending portion of the arch leading towards the brachiocephalic artery, after which it becomes signi cantly reduced (Supplemental Figure  6 ).
The Online Mendelian Inheritance in Man (OMIM) database is a manually curated, daily updated catalog of information for all known mendelian diseases including known causative genes. Despite signi cant advancement in the identi cation of disease-causing mutations using next-generation sequencing, OMIM still contains several hundred mendelian disorders for which the underlying genetic mutation remains unknown  36. Thus, we had the opportunity to overlap the identi ed transcripts with regional-speci c disease, such as moyamoya, patent ductus arteriosus, and site-speci c aneurysms (Supplementary Table  5 ). Through this process, we captured 13 genes (Mcam, Atf5, Cyth2, Ppp1r15a, Tbl1x, Bhlhe4, Wwp1, Stk3, Ywhaz, Klf10, Azin1, Pabpc1, and Gem) that overlapped between site speci c enrichment (this work) and previously identi ed loci for localized vascular disease.
8 M27 for acquisition. Image quanti cation: Images were rst processed with the Imaris le converter then transferred to the Imaris 9. 7 software. Visualization of all the channels was normalized across images from all three anatomic sites. For quanti cation of the relative average intensity of staining for candidate gene. Imaris surfaces were generated using the aSMA+ channel (for cytoplasmic staining) using background subtraction for thresholding. The surfaces were approximately of 10 voxels in size per surface across the entire image for all three regions. This approach allowed us to segerate and restrict our analysis of the channel intensity to only those regions in the image that expressed aSMA+. Subsequently, following surface creation, all of the aSMA+ surfaces in image of the anatomic location were selected and the mean intensity of the site-speci c candidate (Red Channel) per surface was extracted from the statistics table and exported as a new tab delimited le.
Data Availability and Bioinformatic Analysis for Single Cell RNA Sequencing Data The scRNA-seq data sets in this article are deposited in the international public repository Gene Expression Omnibus database under accession code GSE156731, as well as Hadi 2018 (AAA)  45. Sequencing of all three libraries (positional transcriptomic dataset) was performed on the same run on Illumina HiSeq 4000, and the digital expression matrix was generated by demultiplexing, barcode processing, and gene unique molecular index counting. Further processing of scRNAseq involved quality control, normalization, confounding factor identi cation, dimensionality reduction and cell-gene level analysis. Preprocessing of the raw data was conducted following the Cell Ranger pipeline (10x Genomics). To identify different cell types and nd signature genes for each cell type, the R package Seurat (version 3.
2. 2) was used to analyze the digital expression matrix. Cells with less than 500 unique molecular identi ers (UMIs) and greater than 10% mitochondrial expression were removed from further analysis. Seurat function NormalizeData was used to normalize the raw counts. Variable genes were identi ed using the FindVariableGenes function, the top 2000 variable genes were selected for further analysis. The Seurat ScaleData function was used to scale and center expression values in the dataset for dimensional reduction. Principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) were used to reduce the dimensions of the data, and the rst 2 dimensions were used in plots. A graph-based clustering approach was later used to cluster the cells; then signature genes were found and used to de ne cell types for each cluster.
The FindAllMarkers Seurat function was used to nd positive markers of each vSMC population with the Wilcoxon rank-sum test. Pathway enrichment analysis was performed using Metascape. Top 10 categories for each of the clusters investigated as Supplemental Excel File 4. Data visualization (graphs) were generated using ggplot2 package for R. Site Speci c Gene Signature determination: The FindAllMarkers Seurat function was used to nd positive markers of each vSMC population with the Wilcoxon rank-sum test for each anatomic location (3; Supplemental Excel File 1) and subcluster population (6; Supplemental Excel File 3). Following gene list generation, the datasets were ltered for signi cance (padj ≤0. 05) and the presence of shared genes between anatomic sites with duplicate genes if identi ed allocated to the anatomic location (1,045) or vSMC subcluster (1,681) with the most signi cant padj.
Subcluster and Anatomic Location Signature overlap: To identify shared signature genes the nal gene lists for both anatomic location (1,045) and vSMC subcluster (1,681) were overlayed individually using the identify duplicate functions in excel. Genes were characterized as one of 3 major categories: unique to anatomic location, unique to subcluster signature or shared between a speci c anatomic location and speci c vSMC subcluster. From this data, a tabular matrix was constructed with all unique permutations of the 3 major categories (31 patterns combinations were found) and the recording number of genes in each category and data visualization was performed using the tidyverse and ggalluvial packages in R. Gene ontology analysis: Pathway enrichment analysis was performed using Metascape express analysis using default options.
The second matrix (Edge le) contained all gene ontology memberships for visualized genes. Both matrices were then visualized into a custom ontology network using a combination of ggplot2, igraph and ggraph packages for R. "In-silico" forward genetic screen: Genomic loci for mendelian vascular anomalies were identi ed through query, integration, and manual curation of data from multiple online biomedical databases as follows. Given the complexity of mendelian disease nomenclature, vascular diseases/traits were identi ed using the Human Phenotype Ontology (HPO) database (https://hpo. jax. org/app/) using the search term "vascular anomalies" (HP:0002597) which generated 1043 entries. HPO entries were ltered to sub-select for those entries represented in Online Mendelian Inheritance of Man (OMIM) (omim.
Using the UCSC table browser (https://genome. ucsc. edu/) we queried the UCSC SQL database to identify all genes residing in each of the given loci using the knownGene table based on the most current build of the human genome (hg38). The "name" column data from knownGene table for each query was then used to pull data from the KgxRef table to convert gene identi ers to HUGO approved gene symbols. The gene symbols from the regional loci data were then overlaid with the scRNASeq anatomic origin markers for each of the 3 locations. Genes were identi ed as positive hits if they resided withing the genomic coordinates of the loci in question and de ned vSMCs in the arterial bed relevant to the trait. Statistics: For image analysis and animal studies, statistical tests were performed using stats and rstatix packages.
Mcam-based AAA Analysis: To identify different cell types and nd signature genes for each cell type, the R package Seurat (version 3. 1. 2) was used. Neighboring and clustering was performed on the 10 top most signi cant PCAs using a resolution of 0. 5. For Mcam analysis, a cutoff of ≥1 or ≤1 was used to de ne Mcam high and low populations, respectively. For batch correction we used Harmony because it simultaneously accounts for multiple experimental and biological factors and has shown outstanding performance to previously published algorithms particularly for large cell number les. The integrated dataset was then used to t-SNE clustering utilizing the formerly generated Harmony embeddings.
For Mcam-based AAA Analysis: To identify different cell types and nd signature genes for each cell type, the R package Seurat (version 3. 1. 2) was used. Neighboring and clustering was performed on the 10 top most signi cant PCAs using a resolution of 0. 5. For Mcam analysis, a cutoff of ≥1 or ≤1 was used to de ne Mcam high and low populations, respectively. For batch correction we used Harmony because it simultaneously accounts for multiple experimental and biological factors and has shown outstanding performance to previously published algorithms particularly for large cell number les. The integrated dataset was then used to t-SNE clustering utilizing the formerly generated Harmony embeddings.
5 ). The detrimental effects of a reduction in percolation threshold hold for P 50 and P 88 as well. By contrast, it appears from Fig. 6  that better resistance to embolism spread due to redundancy is strongest in the low-pressure portion of VCs (i. e. P 12 ). Therefore, the effect of hci on P 88 is minimal below hci ¼ 3. As a result, increases in P 50 due to pathway redundancy were weaker compared to P 12 but still present. In contrast to hci, the effect of GI on VC measures was weaker. The simulation scatterplots showed P 12 was significantly affected by GI whereas the relationship of GI to P 50 and P 88 was nuanced (Fig. 7 ). Having low GI (c. 1. 5) did not preclude a high P 12 but a high GI constrains P 12 to higher values (Fig. 7a ). The Acer data conform to the scatter plot. The simulations show that P 88 and GI were uncorrelated for the simulations, leaving a weak relationship between GI and P 50 (Fig.
7b, c ). hL v i is a strong predictor of maximum xylem area-specific hydraulic conductivity (k xa,max ; Fig. 8 ) but not GI or hD v i, and therefore the lumen fraction F. As was done for P 12 , P 50 and P 88 , k xa,max is normalized by simulation and by Acer dataset means appropriately (Fig. 8 ). The model correlation between hL v i and k xa,max indicates that water movement through vessel wall pits is the most restrictive to overall sap flow  (Choat et al. , 2008). With longer vessels, sap crosses a smaller number of pit membranes on average, thus increasing the conductivity of the  Average connectivity, <c> Fig. 5  The percolation threshold decreases with increasing average connectivity hci. The percolation threshold is a threshold fraction of conduit-to-conduit contact walls that allow an adjacent embolism to spread through them at a given xylem sap tension.
7a ) while maintaining a high percolation threshold (Fig. 5 ) by keeping hci to values low enough to prevent the explosion in the speed of embolism spread (Fig. 4 ). This supports the hypothesis that increasing GI is a necessary but insufficient condition to improve Acer resistance to embolism spread. Nonetheless, trends between GI and PLC measures are similar, but weaker, to those with hci despite the fact that we varied the fraction of tangential and diagonal connections four-fold in the simulations (Figs 7, S1). Most striking is the absence of a linear relationship between GI and P 50 but its presence in the Acer dataset  (Lens et al. , 2011). This suggests that the trend stems from the significant correlation between GI and another pit membrane property. This assertion is corroborated by a linear relationship between GI and P 88 in the dataset (Fig.
If increasing hci leads to improved xylem resistance to embolism spread without negatively affecting hydraulic flow efficiency, then what might be the evolutionary drivers of hci variation between species? The fact that the ubiquitous GI measurements could be uncorrelated to measures of 3D connectivity (Fig. 9 ) means that, currently, the data needed to answer this question are unavailable. If, however, hci is correlated with GI and air-seeding is the main way for embolisms to spread, then species such as those in the genus Acer might have to increase hci at the expense of increasing vessel frequency. This is because GI and VA À1 x are highly correlated in Acer  (Lens et al. , 2011). This would incur one of two costs: either there would be less space for Fig. 8  While the Acer dataset (dark gray circles) shows significant correlations between the maximum xylem area-specific hydraulic conductivity (k xa, max ) and (a) the grouping index (GI) and (b) average vessel length (hL v i), simulations show that only hL v i is a significant predictor of k xa,max (blue and light gray circles).
[23, 24]  used aerosol AOD and AAOD products retrieved from POLDER-3/PARASOL polarimetric data for constraining global emissions of aerosols. Moreover, Li et al. [18]  developed in the frame of GRASP algorithm  [21, 25]  an approach designed for direct retrieval of main aerosol components from directional polarimetric satellite observations. The successful applicability of this approach was illustrated in several studies  [26, 27]. In this study, we explore the utilization of this approach in conjunction with analysis of the air quality and meteorological data. Specifically, we derive accurate spatial distribution of aerosol optical, microphysical properties and aerosol components from POLDER/PARASOL satellite observations over NCP. We also focus on the pollution haze events that occurred over Beijing in January 2012.
The spatial resolution at nadir is 5. 3 km × 6. 2 km. POLDER-3 can observe up to 16 view angles for each pixel. The POLDER-3 mission was accomplished in 2013 and it has provided about nine years of valuable data, which can be downloaded from http://www. icare. univ-lille1. fr/archive? dir=PARASOL/ (accessed on 25 May 2020). The full archive of the POLDER-3 data was processed by GRASP algorithms and several extended aerosol products were generated  [19]. The extended validation and analysis of Chen et al. [32]  demonstrated several advantages of MAP 10 km products compared to the conventional products provided by single viewing images. Schutgens et al. [33]  analyzed the consistency and difference of different satellite aerosol degree products and demonstrated the reliability and unique value of MAP aerosol products especially in regard to such property as aerosol absorption.