dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, we also included the building period as further descriptor of the urban environment. INSEE data on the housing stock  [51]  is freely accessible. The latest dataset on the housing stock dates back to 2014 and is the one used in this work. In the 426 census areas of the French Riviera, there are a total of 712,778 properties, 492,050 of which are main residences, 159,678 are second homes, and 61,050 are vacant.
Public Transport Data We relied on two sources for obtaining information on public transport hubs for the area under exam: OpenMobilityData [52], a web platform that collects and makes available for download open-source public transit data from around the world, and OSM [53], probably the most renown open platform for geographic crowd-sourcing. The former provided public transit data for the network Lignes d'Azur, the transport company that serves the metropolitan region of Nice. Such data consist of georeferenced points indicating public transport hubs, such as bus stops and stations, and means of transport (e. g. , bus and tram). We obtained a total of 2691 public transport hubs for the city of Nice. To fully cover the study area, we integrated this information with OSM data. This was extracted from Geofabrik  [54] , a website that periodically collects, organises, and makes available for download OSM data, in shapefile format, for regions and sub-regions of the world.
Street Value After having obtained PERVAL data for the portion of French Riviera under exam, we firstly performed a filtering and then applied the pre-processing presented above, before computing classes of housing values at the street level. Firstly, we filtered out those transactions that could have biased the analysis, that is properties with special contractual agreements (e. g. , retirement homes), and those that did not have sufficient spatial accuracy (e. g. , those geocoded at the municipal level). Secondly, since PERVAL does not provide housing types (an information required by our methodology), we created such types by combining the broad categorisation provided by PERVAL (i. e. , flat or single house) with the number of rooms. By doing so, we ended up with seven housing types:.
• T1 for studios and one-room flats; • T2 for two-room flats; • T3 for three-room flats; • T4 for four-room flats: • T5 for five (or more)-room flats; • PM (petites maisons, small houses) for houses with up to four rooms; and • GM (grandes maisons, big houses) for houses with five or more rooms. We then segmented the dataset according to year of transaction and housing type and computed ventiles of prices. Finally, we calculated the median of such values for spatial units comprising each street segment and its direct neighbouring streets. In Figure  2 , we present a map of such medians (i. e. , our street values) for the 14,319 street segments of the French Riviera, where a minimum of 10 transactions were recorded in the period 2008-2017. Values vary between 1 (the least valued streets) and 20 (the most valued ones) and constitute the target variable of our ML ensemble method.
Limitations Although PERVAL is an official dataset managed and kept updated by the French association of notaries, the geolocalisation of some observations might still be imprecise. Furthermore, Casanova et al. remarked that some housing transactions are missing in PERVAL and that particular attention should be given to the treatment of transactions concerning several items  [59]. These issues might have thus introduced inaccuracies in the computation of our metric of street value. By focusing on spatial units comprising of each street and immediate neighbouring streets with more than 10 transactions, we lost part of the information present in the original housing transaction dataset. Although we have done so to make our findings more statistically robust, such data might have helped to provide a more complete picture of the relationship between our metrics of the urban environment and values at the street level.
Identifying grassy biomes. Our dataset provides the first spatially explicit, functional classification of grassy vegetation at the global scale (Figs. 1  and S1 ). Necessarily, our approach that focusses on the ground layer contrasts with efforts to map biomes using remotely sensed tree cover or biomass  (4, 29). Such studies generally misclassify extensive areas of tropical savanna as forest or degraded forest  (30, 31). Global synthesis of grassy biomes has been prohibited as satellite remote sensing does not see through a tree canopy. Therefore, we mapped grassy formations by integrating and re-classifying 20 existing national and regional vegetation maps produced using botanical data and detailed vegetation descriptions (see.
Why do three of the most diverse grass lineages characterise grassy biomes? Does diversity beget ecological success or does success beget diversity? Early diversification may have enabled ecological success, such that ecological speciation allowed each lineage to radiate across broad environmental envelopes (an ecological mechanism). Alternatively, a neutral mechanism of a long history of diversification may have led to high diversity as Andropogoneae and Chloridoideae are the oldest C4 lineages. Across our dataset, evidence for this is equivocal. We list 8. 8% of all grass species and within lineages: Andropogoneae, 14. 5%; Chloridoideae, 6. 5%; Pooideae, 10. 8%. Perhaps ecological success facilitated diversification, such that large geographical ranges enabled by unique adaptations made the isolation of populations and allopatric speciation more likely (a geographic mechanism).
Methods Classifying grassy biomes. Data from 20 vegetation maps derived from botanical information, or a combination of botanical and geographic information, were integrated to delineate grassy biomes (references for these maps are listed in the Supplementary Information). The result was a global map of grassy biomes resolved into 1,635 discrete vegetation units, each defined by its characteristic grass species, which formed a list of 1,154 species (accounting for synonymy) found commonly across global grassy biomes. Vegetation maps are generally based on botanical survey and geographic analysis, combined with expert input, that cluster species composition and vegetation structure to define unique vegetation units. We compiled the ground layer information for the vegetation units in each map to identify the grass species considered to characterize a vegetation unit.
To determine whether vegetation units were naturally dominated by grasses, we developed a set of criteria. First, artificial vegetation units were defined as those plowed or sown for agriculture and where humans are planting species that would not otherwise occur. We retained data for this analysis of only natural formations. Second, based on the vegetation descriptions we determined whether > 50% of the relative ground cover or biomass was derived from grasses. We used this definition in place of 'Is there a continuous grassy ground layer?' because low herbaceous cover in predominantly grassy vegetation would present a problem with the classification of desertic and arid environments. Vegetation units were considered grassy deserts where the total above-ground biomass was considered <50 g m 2 , or where total ground cover <25%, throughout the year.
5-degree grid cell occupied by grassy polygons. Using the grass phylogenetic and trait information compiled, we then calculated the occupancy of grassy polygons by photosynthetic type, annual/perennial life history, grass lineage, and mean maximum grass height. These data are not the same as a classic concept of abundance or dominance but are a relative measure of the likelihood of occupancy measured from zero to unity. We undertook a validation of our map compilation described in the Supplementary Information and in Figure  S9. Phylogenetic and plant trait information. We cross-referenced our species list to a taxonomy of accepted scientific names (GrassBase, http://www. kew. org/data/grasses-syn/cite. htm) and a recent accepted phylogeny from the Grass Phylogeny Working Group  (25)  to eliminate synonymy and link species to descriptions of evolutionary history and functional traits.
We included annual/perennial as while most grasses reach sexual maturity in < 1 growing season, perennial grasses can be longlived. Plant longevity is an effective strategy for occupying space in competitive environments  (63). We summarized these data for each grassy vegetation unit based on the grass species listed as characteristic of each unit. For the Poaceae species that we listed, we extracted all available georeferenced occurrence records from the Global Biodiversity Information Facility (GBIF) web portal (http://www. gbif. org/; accessed January 2014) and cleaned these data to ensure longitude and latitude values were viable and to two decimal places. Species distributions were standardised against descriptions of distributions in Grassbase using TDWG regions. For this subset of species produced via distribution records, median fire return intervals were calculated at a species level following the methods of  Archibald et al.
2010 (64). Information on fire date was extracted for each GBIF location from the MODIS global monthly burnt area (MCD45A1) satellite data product. To calculate climatic extremes for these same species, the WorldClim dataset (www. worldclim. org) was used to obtain species median values of minimum temperature (BIO6 variable) and seasonal drought length (calculated as the number of successive months where mean annual precipitation was below 30mm). These species level data were used to construct frequency histograms to examine lineage level variation in fire regimes and climate extremes  (Fig S7). Environmental data used in global analyses. Our analysis aimed to elucidate lineage, climate and disturbance relationships, and whether biogeography impacts the C3-C4 crossover temperature. We used the WorldClim dataset at a 0.
5 degree resolution to match the vegetation map, and extracted mean annual precipitation (MAP), rainfall seasonality, mean annual temperature (MAT) and temperature seasonality (www. worldclim. org). We used a rainfall concentration index to describe rainfall seasonality based on  (35). Growing season temperature (GST) was calculated for each grid cell to quantify regional and global C3-C4 crossover temperatures. GST was calculated as the mean temperature across months with a greater than or equal to 5 degree mean temperature and at least 25 mm rainfall, and was calculated using WoldClim monthly climate normals  (65). A median fire return interval (FRI) is the number of years between fire events that represents the time period available for plants to grow. We used fire interval data from the 16 year MODIS fire datasets to fit Weibull distributions to 0.
5° gridded data for the globe by using the method outlined in  Archibald et al. 2010 (64). Tropical grasslands and savannas have the world's shortest fire return times, due to rapid rates of fuel accumulation and a climate that supports frequent fire (annual dry seasons, warm climate and reliably seasonal rainfall)  (20). Our dataset of estimated fire return times, while spatially biased, is therefore robust for grassy biomes. Globally consistent data on present or past herbivore pressures are simply not available. We were obliged to restrict our analyses to Africa where efforts have been made to map mammalian herbivore pressures of both wildlife and livestock  (66). We combined herbivore and fire data to assess links between lineage composition and disturbance. Soils data are not of sufficient quality to be meaningfully incorporated in analyses of this scale, despite being known to mediate local scale vegetation patterns  (35).
To produce these plots, an environmental dataset was simulated where the focal environmental variable is varied over its full environmental range and where, for each interval, the observed median of each of other environmental variables (median over areas where the focus environmental variable is within the interval) is returned. The displayed curve in each case is the prediction of our Regression Random Forest model over this simulated dataset. The process used bivariate response curves, where two variables rather than one vary simultaneously. The 90 th quantile of a kernel density function (function kde2d from the R package ade4) was used to plot limits of grass lineages relative to herbivore abundance and fire frequency.
Development and validation of map compilation Rarely, if ever, has this rich body of vegetation mapping research been integrated with Earth system science or evolutionary studies. This is perhaps because vegetation mapping is We undertook a validation process between plot data describing in situ grass abundance and our global species list. Using publicly available data that intersected with vegetation unit descriptions we found that, at the level of independent evolutionary lineages of grasses (i. e. , subfamily), we had strong confidence in the geographic and environmental relationships we elucidate here (Fig. S9 ). To validate the classification of common grass species across regions, we compared the species list in each vegetation unit to a plot level database developed for validation purposes (Fig.
S9 ). Plot data were sourced from the literature and vegetation databases and assembled by the authors (see references in the Supplementary Information). 110 vegetation units contained enough plot level data for validation analyses. To determine what taxonomic levels agree with plot data, the comparison was conducted at the species and subfamily levels. We also examined the agreement of our map and plot datasets at a functional level by comparing the attribution of photosynthetic type. From the 507 common grass species across these vegetation units, 88% of these species were present in the plot dataset of those appropriate vegetation types. This is a very high degree of overlap in species in our mapping classifications and plot data, especially considering the difference in scale between local species plots and large vegetation units.
Furthermore, we found that vegetation types generally had similar percentages of characteristic grass species represented in their plot datasets, although the agreement was worse for particularly large and broadscale vegetation units. To validate the higher taxonomic classifications and plant functional type classifications of our map units, we compared the proportion each classification in plots (weighted by abundance) to the proportion of that classification in our map. Because these data are on the interval (0,1) we used beta regression to model this relationship. Beta regression can be interpreted much like logistic regression, except that it allows continuous values in the dependent variable. Proportions of Poaceae subfamilies and functional types showed that plot values were strongly predictive of classified values in our map (Fig.
Koonamore Research Group. (2015) Kangaroo Transects. Koonamore Vegetation Monitoring    Project (1925-present) , Version 12 /2014. AEKOS Data Portal (http://www. aekos. org. au/home). Accessed: 16 July 2014. Koonamore Research Group. (2015) Photopoints. Koonamore Vegetation Monitoring Project (1925-Present), Version 12 /2014. AEKOS Data Portal (http://www. aekos. org. au/home). Accessed: 16 July 2014. Koonamore Research Group. (2015) Myoporum platycarpum Survey. Koonamore Vegetation Monitoring Project (1925-Present), Version 12 /2014. AEKOS Data Portal (http://www. aekos. org. au/home). Accessed: 16 July 2014. Koonamore Research Group. (2015) Rabbit Activity Monitoring and Control. Koonamore Vegetation Monitoring Project (1925-Present), Version 12 /2014. AEKOS Data Portal (http://www. aekos.
org. au/home). Accessed 16 July 2014. Koonamore Research Group. (2015) Saltbush Transects. Koonamore Vegetation Monitoring Project (1925Present), Version 12 /2014. AEKOS Data Portal (http://www. aekos. org. au/home). Accessed: 16 July 2014. Koonamore Research Group. (2015) Senna Populations. Koonamore Vegetation Monitoring Project (1925-Present), Version 12 /2014. AEKOS Data Portal (http://www. aekos. org. au/home). Accessed: 16 July 2014. Koonamore Research Group. (2015) Senna Quadrat (Cassia Corner). Koonamore Vegetation Monitoring Project (1925-Present), Version 12 /2014. AEKOS Data Portal (http://www. aekos. org. au/home. Accessed: 16 July 2014. Koonamore Research Group. (2015). Vegetation Quadrats. Koonamore Vegetation Monitoring Project (1925-Present), Version 12 /2014. AEKOS Data Portal (http://www.
Nonetheless, the slight increase in epigenetic age we observed in old stem cells also suggests that cell-intrinsic changes are also likely to play a role. These changes could be related to cell metabolism or cell division. In this scenario, extrinsic and intrinsic factors would contribute to the epigenetic clock. Technical factors, such as the datasets used to train the model, could also contribute to these predictions. Future single cell studies including samples from different ages will be crucial to determine the specific role of cellular composition and cell-intrinsic changes in the epigenetic clock. Previous studies have investigated transcriptional heterogeneity changes with age in mixed cell populations  30  , which are affected by differences in cellular composition, such as an increase in senescent cells  30.
Single-cell RNA-seq libraries were aligned using HiSat2 with options-sp 1000,1000-no-mixed-no-discordant  38. Single-cell bisulfite libraries were processed using Bismark 39 as described  6. Mapped RNA-seq data were quantitated using the RNA-seq quantitation pipeline in Seqmonk software (www. bioinformatics. babraham. ac. uk/projects/seqmonk/). Quality control RNA-seq. Cells expressing fewer than 1000 genes or <10 5 mapped reads allocated to nuclear genes were removed in quality control (Supplementary Figs. 15, 16). These cells were also verified to have less than 10% of mapped on mitochondrial genes (Supplementary Figs. 17A, B, 18A ). Out of the 768 cells that were captured across the experiment from four young and two old mice, 377 passed our quality and filtering criteria (Supplementary Table  1 ).
All samples showed similar distribution of reads and numbers of detected genes (Supplementary Figs. 17C, D, 18C ). Data analysis RNA-seq. Gene expression levels were estimated in terms of reads per million of mapped reads to the transcriptome. A score of variability per gene (named distance to the median) was calculated by fitting the squared coefficient of variation as a function of the mean normalised counts and then calculating the distance to a rolling average (window size = 100, Supplementary Fig. 19 )  21. We included only genes with an average normalised read count of at least 10. The top 1000 most variable genes of the entire data set were used to perform principal component analyses (as log 2 -transformed and median-cantered values) (Fig. 1b , Supplementary Data 2). Single cell differential expression (SCDE) was used to calculate differential expression analysis between young and old cells (Supplementary Data 1)  40.
We then performed Spearman's correlation analyses to assess the similarity between each cell from old samples and the young transcriptome. Spearman's correlation analyses were then also used to find gene expression patterns associated with this genome-wide similarity score. Genes expressed in fewer than five cells were excluded from the analysis. The top 200 correlated and anticorrelated genes (Supplementary Data 3) were used for GO enrichment analysis  41. Modelling the epigenetic clock. Epigenetic ages of four samples (two young and two old, Supplementary Table  1 ) were calculated using a method based on the clock published in Stubbs et al. 25. To predict the ages on the four single cell samples we first aggregated all the reads per cell by summing the methylation calls. We then took the two largest datasets used in the original publication, the Stubbs et al.
data  25  and the Reizel et al. data  42  , intersected them with the requirement that sites are found in at least 70% of the samples of these joint sets. Subsequently sites that were not covered 5× in all the merged single cell samples were removed. Lastly, all training samples that did not have 100% coverage of the sites obtained by these intersections were removed. This left 70,060 sites in 144 samples of the combined Reizel et al. and Stubbs et al. datasets. We used the same modelling approach to build the clock as described in the Stubbs et al. paper, with one exception; the methylation was binned in values of 20%, based on rounding off the methylation calls to assure there is no read-depth information encoded in the methylation calls. DNA methylation heterogeneity. Cells that had less than 1 million paired-end alignments or less than 500,000 CpG sites covered were discarded (Supplementary Fig.
9 ). To avoid biases that might occur due to different sequencing depths or number of cells between individuals, the data was down-sampled to 1 million reads for each cell and randomly selected 35 cells from each individual (two young and two old, Supplementary Table  1 ). ChIP-seq datasets for H3K4me3, H3K27me3, H3K36me3 in muscle stem cells and H3K27ac in myoblast were obtained from existing studies  20, 27. Bowtie2 and MACS2 were used for mapping and peak calling respectively. We developed a heterogeneity score based on Hamming distances and Shannon entropy between cell pairs from the same sample. This value captures the properties we desire: (i) ability to detect cell-to-cell stochastic heterogeneity; (ii) not affected by population substructure; (iii) not biased by missing values. Specifically, let r be a matrix with methylation values of cells for a particular gene, each row corresponding to a cell and each column corresponding to a CpG site, and w be the weight corresponding to the number of covered CpGs within each pairs of cells.
Acknowledgements We would like to thank the  Flow Cytometry Platform of the Center for Technological Resources and Research (Institut Pasteur) , the  Wellcome Trust Sanger Institute sequencing facility  for assistance with Illumina sequencing and SciArtWork for producing illustrations. We also thank  Laura Benson  for assistance with library preparation. This project was supported by grants from  Institut Pasteur ,  Agence Nationale de la Recherche  ( Laboratoire d'Excellence Revive, Investissement d'Avenir ;  ANR-10-LABX-73 ),  Association Française contre les Myopathies  ( 21857 ),  CNRS , the  European Research Council  ( Advanced Research Grant   332893 ), and the  Biotechnology and Biological Sciences Research Council (BBSRC ,  CBBS/E/B/000C0425 ). I. H. -H. was supported by a.
Chronic kidney disease is associated with an increased cardiovascular risk, and altered biological properties of high-density lipoproteins (HDL) may play a role in these events. This study aimed to describe the HDL proteome from non-diabetic hemodialysis patients and identify potential pathways affected by the dysregulated expression of HDL proteins. HDL were sampled from nine non-diabetic hemodialysis (HD) and eight control patients. Samples were analyzed using a nano-RSLC coupled with a Q-Orbitrap. Data were processed by database searching using SequestHT against a human Swissprot database and quantified with a label-free quantification approach. Proteins that were in at least five of the eight control and six of the nine HD patients were analyzed. Analysis was based on pairwise ratios and the ANOVA hypothesis test.