dataesr/scientific-paragraphs-categorization huggingface dataset:
Acquisition of microCT data and 3D geometric morphometrics Supplementary video related to this article can be found at https:// doi. org/mmcdoino Right petrosal bones from 102 individuals were scanned at the 'Institut de Mécanique des Fluides de Toulouse', France (IMFT, N = 100) as well as at the 'Centre Interdisciplinaire de Recherche et d'Ingénierie des Matériaux' de Toulouse, France (CIRIMAT, N = 2) using X-ray microtomography with a scan resolution ranging between 0. 033 and 0. 066 mm (EasyTom XL Micro, RX solutions). A total of 94 specimens were scanned following DNA analysis, whereas nine petrosal bones were first processed for microCT and then for DNA. All specimens provided sufficient DNA material for sex and taxonomic identification. MicroCT scans were first imported into the Avizo v8.
Statistical analyses Shape analyses were performed using R studio software (https://rst udio. com/) and consisted of procrustean and principal component analysis as implemented in the geomorph package  (Adams et al. , 2016; Adams and Otárola-Castillo, 2013). We considered different sets of landmarks for each structure, including 40 landmarks for the cochlea and 20 landmarks for each semi-circular canal (SCC) curve. Linear Discriminant Analysis and predictive scores for both sex and species were produced using a leave-one-out cross validation and confusion matrices. Several metrical characteristics of the bony labyrinth, including the length of the cochlea and each SCC and the number of cochlea turns  (Braga et al. , 2015) , were measured using the MATLAB software (https://fr. mathworks. com/).
02 × 10 -2 ) and sex and taxonomical identification success, indicating that the procedures followed for acquiring 3D models of the inner ear were not significantly detrimental to DNA. Further processing of sequence alignments against the horse nuclear and mitochondrial reference genomes provided sufficient equine reads for both sex and species determination (N ≥ 162, 902-420,818,091; see Fig. 3  for typical Zonkey assignments). This provided a total of 41 horses (27 males, 14 females), 24 donkeys (20 males, 4 females), 36 mules (16 males, 20 females) and one hinny female for characterization of the inner ear morphology. The limited proportion of hinnies in our dataset is on par with previous reports  (Carette 2003; Chaix 2016 ), indicating anatomical, behavioral and genetic issues limiting production outcomes  (Allen and Short, 1997).
Geometric morphometric (GMM) analysis We used microCT to generate the 3D models of 102 ancient equine right bony labyrinths for which DNA provided unambiguous sex and taxonomic identification. In order to ensure balanced sampling across all sex and taxonomic categories, DNA analyses were carried out prior to imaging for the vast majority of the specimens (93/102). Ancient DNA analyses are, however, destructive and even careful drilling was found to damage the inner ear morphological structures of 20 specimens (including 6 male horses, 6 horse mares, 2 donkey jacks, 5 male mules and one female mule). This left a total of 82 specimens with both cochlea and SCC data for which the full set of morphological analyses could be carried out. Morphological analyses on the entire panel of 102 specimens were limited to the SCC, in the absence of cochlear information.
Principal component analysis Shape variation within our data set were analyzed following Procrustes transformation of the landmarks coordinates so as to eliminate possible size covariates. Principal component analyses (PCA) of the cochlear and the SCC shape variables, either taken separately (Fig. 4A  and B ) or altogether (Fig. 4C ), revealed relatively limited overlap between horses and donkeys, suggesting that both species may be successfully discriminated morphologically. However, mules occupied an intermediate position that largely overlapped both parental clusters. Interestingly, the single hinny individual available clustered within the mule morphospace and both hybrids showed larger morphological overlap with the donkey cochlea than that of horses (Fig. 4A ), while their SCC appeared closer to those of horses than donkeys (Fig.
Discussion In this study, we gathered an extensive collection of equine archaeological petrosal bones consisting of 102 specimens from France and Turkey that were spread across the last ~2,000 years. This collection was aimed at encapsulating significant morphological shape variation amongst horses, donkeys and their hybrids. We leveraged a method based on ancient DNA data that was recently developed by some of us  (Schubert et al. , 2017)  to unambiguously determine the molecular sex and taxonomic status of each individual bone material. We then subjected them to a range of morphological analyses in order to assess their respective predictive power for sex and taxonomic determination. Our goal was to mitigate some of the limitations of the methods currently available based on morphological analyses of equine remains that have shown limited performance due to the selection of somewhat debatable classification criteria  (Baxter, 1998; Chuang and Bonhomme, 2019; Geigl and Grange, 2012; Granado et al.
Acknowledgements We thank all members of the  AGES (Archaeology, Genomics, Evolution and Societies)  and  IDEA (IDentification, Environment and Anthropometry) research groups at CAGT (Centre for Anthropobiology and Genomics of Toulouse)  for fruitful discussions. The authors would like to thank  Alessio Bandelli  (Inrap),  Maude Barme  (CRAVO),  Alice Bourgois  (CRAVO),  Franck Decanter  (Inrap),  Julie Rivière  ( Service Archéologique de Chartres ),  Nicolas Morand  ( UMR 7209 ) and  Olivier Putelat  ( Pôle d'Archéologie Interdépartemental Rhénan-Archéologie Alsace ) for their help in sampling, and the archaeologists who provided the dating elements and contexts. This work was supported by the  France Génomique Appel à Grand Projet  ( ANR-10-INBS-09-08 ,  BUCE - PHALE  project; the  Danish National Research Foundation  ( DNRF94 ); the  Initiative d'Excellence Chaires d'attractivité, Université de Toulouse (OURASI)  and the  Villum Fonden miGENEPI research project.
Pierre Clavel 's PhD position is funded by the  CNRS MITI interdisciplinary programme (Mission pour les Initiatives Transverses et Interdisciplinaires). Andaine Seguin-Orlando  acknowledges  IAST  funding from  ANR  under grant  ANR-17-EURE-0010  ( Investissements d'Avenir program ). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement  681605 ).
The satellite data used to study these changes were provided by the TROPOMI sensor of the Sentinel-5P satellite. The TROPOMI sensor measures the concentration of various atmospheric pollutants using optical depth  (Wei et al. , 2020). Monitoring data from on-site stations were also used to compare with the satellite data. In France, the first lockdown was imposed on March 17, 2020, lasting until May 11, 2020. Mobility was reduced by 79% during these lockdown restrictions  (Galeazzi et al. , 2020) , and industrial activities and secondary services ceased. Several studies have also addressed the effect of lockdown on air quality in France  (Ikhlasse et al. , 2021; Magazzino et al. , 2020; Piazzola et al. , 2021; Sbai et al. , 2021). The slowdown in the economy and reduction in mobility improved air quality in several urban settings; thus, it is important to study the actual depollution effects of COVID-19 lockdown.
Data The data used in this study were satellite data from the Sentinel-5P which was launched in 2017. The satellite sensor on board Sentinel-5P is known as TROPOMI. This sensor collects global data for air quality assessment. The TROPOMI instrument is a multispectral sensor that records the reflectance of significant wavelengths to measure atmospheric concentrations of O 3 , CH 4 , HCHO, aerosol, CO, NOx, and SO 2 , as well as the characteristics of clouds at a spatial resolution of 0. 01 • (ESA -Copernicus, n. d. -b). This study investigated the changes in atmospheric concentrations of NO 2 , HCHO, SO 2 , O3, CO, and UV aerosol index  (Kovács, 2021d). In the case of NO 2 , the tropospheric column concentration closest to the surface was investigated. Sentinel-5P provides data on the total, tropospheric, and stratospheric NO 2 concentrations.
Meteorological data from satellites were also used to study meteorological conditions (temperature, total precipitation, dew point, atmospheric pressure at sea level, atmospheric pressure at surface level, wind u and v components, wind speed, humidity and solar radiation). These data were retrieved using the Google Earth Engine from ERA5 (European Center for Medium Range Weather Forecasts -ECMWFreanalysis) (Copernicus Climate Change Service, n. To validate the satellite data, monitoring data from stations located in the cities of Grand Est were used. These data were obtained from the European Environment Agency (EEA) database  (EEA, 2020b; EEA, 2020a). In addition, statistical data related to the population and economic development of the ten urban areas of Grand Est were used. These data were obtained from the Eurostat database  (Eurostat, n.
Methodology The working methodology of this study was to model the change in the atmospheric concentration of some pollutants (NO 2 , HCHO, SO 2 , O 3 , CO, and AAI) during the COVID-19 lockdown period. This change was calculated from satellite column density data measured between two dates marking the lockdown period in which certain measures to stop the spread of coronavirus were implemented. In this case. column density refers to the total amount per unit area of a material suspended in the atmosphere, measured along the length of the column. As previously stated, the data of Sentinel-5P began from November 22, 2018 T12:00:13; therefore, the period of comparison was limited to the year 2019. The change in air pollutants due to the implementation of anti-COVID measures was analyzed by calculating the arithmetic difference between the standings of 2020 and 2019, considering the same reference period (March 17-May 11).
2 ). The definition of the reference period was established considering the steps undertaken by the French government and the local administration to stop the spread of the virus. The reference period of this study corresponds to the first lockdown phase in France, which was generalized to the ten metropolitan areas that were investigated in this study. Therefore, the dates covering this period are March 17, 2020, to May 11, 2020. The same period of days (56 days) was applied to the raster in the filter of 2019. Obtaining and downloading the rasters of NO 2 , SO 2 , CO, HCHO, O 3 , and aerosol index were performed using the Google Earth Engine application. The original Sentinel 5P Level 2 (L2) data were grouped by time, not by latitude and longitude. For satellite data to be entered into the Google Earth Engine, each Sentinel 5P product at Level L2 becomes Level 3 (L3) by conversion.
This is done by keeping a single grid per orbit; in other words, no aggregation is performed between the products. The original products that encompass antimeridian were ingested as two Earth Engine assets, with suffixes _1 and _2. The conversion to Level (L3) was carried out using the harpconvert tool through the bin_spatial operation, in which the satellite products can be processed with other HARP-type commands through code lines. Therefore, the filtering of the satellite data was performed with code lines in the Earth Engine. Google Earth Engine called the COPERNICUS/S5P/NRTI satellite database, performed date filters (. filterDate()) and spatial extension filters (. filterBounds(AOI)), and represented the data using statistical methods such as min(), max(), and mean(). In this case, the pixel values were represented using.
median() filter. The data for each variable were represented by selecting (. select()) the raster band containing the tropospheric or numerical column density information. Representation over a given geographical area (AOI) requires using a clip layer that delimits the study area,. clip(AOI). Finally, the download is performed by exporting the same raster with the command Export. image. toDrive(). The code is available at the following address (requires a registered Google Earth Engine account): https://code. earthengine. google. com/e 9fcf98c03da3200892cace2d6cf15be  (Kovács, 2021d). The arithmetic differences between the 2020 and 2019 raster images were calculated using the QGIS raster calculator (QGIS. org, 2021). Therefore, the arithmetic difference between the pixel values of and 2019 revealed where it decreased (negative value) and where it increased (positive value) the concentration of the given atmospheric pollutant.
Using the original data and the difference data obtained, three types of results were generated: maps and difference graphs, comparison of distributions, and representation of differences within the categories of soil cover. Representative samples were taken from the difference rasters to model the magnitude of the distribution of change. The number of representative samples was calculated using a finite sample size calculation equation (Eq. 1)  (Darling & Robbins, 1967). The population size (N) was considered as the total number of pixels that covered the study area (9010457 pixels). Therefore, a sample size with a margin of error of 5% was searched for 385 observation samples. n = N * Z 2 α * p * q e 2 * (N -1) + Z 2 α * p * q (1) where n is the size of the sample sought, N is the size of the population or universe, Zα is the statistical parameter that depends on the confidence level, e is the accepted maximum estimation error, p is the probability that the event has occurred, and q = (1 -p), which is the probability that the event will not occur.
A. 9). The statistical tool Raster layer zonal statistics available in QGIS was used to represent the average difference produced in the land cover categories within the different metropolitan areas of the region. The seasonal meteorological factors were eliminated by homogeneity analysis of meteorological data using the chi-square test (χ2). For this purpose, daily data were obtained for each EPCI urban area between March 17 and May 11 for nine meteorological factors that may influence the concentration of pollution. The nine factors are: temperature, total precipitation, dew point, atmospheric pressure at sea level, atmospheric pressure at surface level, wind u and v components, wind speed, humidity and solar radiation. These data originated from three satellite products: ERA5 (ECMWF re-analysis), GLDAS-2.
The chi-square statistical test (χ2) for homogeneity was defined as follows (Eq. 2): χ 2 h = nm ∑ r i=1 ( vi n -μ i m ) 2 v i + μ i (2) where v i is the observation of the first distribution, μ i is the observation of the second distribution, n is the total number of observations of the first distribution, and m is the total number of observations of the second distribution. In our case, the total number of observations was 56 (days) for both distributions. The codes from which these meteorological data were obtained are available at the following links: ERA5 (ECMWF reanalysis): https://code. earthengine. google. com/c8e51091665cc4e b409b69614bac5ffc  (Kovács, 2021a) , GLDAS-2. 1: https://code. earthe ngine. google. com/7f9d0346778cab602e3d31eb5dabbc5f (Kovács, 2021c) , and ERA5-Land: https://code.
Multiple distinct cell types of the human lung and airways have been defined by single cell RNA sequencing (scRNAseq). Here we present a multi-omics spatial lung atlas to define novel cell types which we map back into the macro-and micro-anatomical tissue context to define functional tissue microenvironments. Firstly, we have generated single cell and nuclei RNA sequencing, VDJ-sequencing and Visium Spatial Transcriptomics data sets from 5 different locations of the human lung and airways. Secondly, we define additional cell types/states, as well as spatially map novel and known human airway cell types, such as adult lung chondrocytes, submucosal gland (SMG) duct cells, distinct pericyte and smooth muscle subtypes, immune-recruiting fibroblasts, peribronchial and perichondrial fibroblasts, peripheral nerve associated fibroblasts and Schwann cells.
As a demonstration of the depth and novelty of our atlas, we transcriptionally defined chondrocytes for the first time in human lungs (ACAN, CHAD, COL9A3, HAPLN1 and CYTL1) (Figure  S1 c, d ) and mapped these cells to the cartilage with Visium ST (Figure  1 c, d ). Chondrocytes were mostly released using single nuclei sequencing from trachea (Figure  1  e, Figure  S1 e-g ), demonstrating the utility of our multi-omics, multi-location atlasing of the human lung. Overall, we generated a large scRNAseq and snRNAseq dataset with wide representation of distinct cell lineages and matching spatial gene expression data, allowing further exploration of cellular heterogeneity and possible cell-cell interactions to better understand lung function. Identification of rare fibroblasts with immune recruiting properties.
Resolution of the vascular cell types in the systemic and pulmonary circulation We next focused on studying of the vasculature, which in the lung consists of both systemic circulation providing oxygen to the tissue, and pulmonary circulation where gas exchange occurs. Uniquely in our dataset, we could distinguish clusters of pulmonary and systemic circulation by the distribution of cell types, where pulmonary cells are enriched in parenchyma and systemic cells are enriched in the trachea (Figure  3d , Supplementary Figure  S7e ). We observed previously described venous (E-Ven-syst, E-Ven-pulm) and capillary (Cap, Car4-Aerocyte) endothelial cell types (Figure  3a , Supplementary Figure  S7a ) (Vila  Ellis et al. 2020; Niethamer et al. 2020; Gillich et al. 2020; Schupp et al. 2021). In addition, we now distinguish arterial endothelial cell types (systemic E-Art-syst and pulmonary E-Art-pulm), which have distinct localisation on Visium ST with E-Art-syst enriched in the trachea (Figure  3a, b ).
Data availability Sequencing data for scRNAseq, snRNAseq, VDJ-seq and Visium ST will be available via the DCP (http://data. humancellatlas. org). Sc/snRNAseq data will be available for browsing and gene expression data download via cellxgene website. and myoepithelium (FHOD3) cell type markers in human bronchus section. (h) H&E from bronchial section and Cell2location density values for mapping duct, mucous, serous, ciliated and myoepithelial cells onto the Visium ST section. (i) Linear mixed model analysis revealed 80 differentially expressed genes in ciliated cells between trachea and other locations (bronchi and parenchyma). Violin plots of normalised log-transformed expression separated by location in the single nuclei RNA-seq data for three representative genes upregulated in nasopharyngeal carcinoma gene set from GSEA database with LTSR>0.
The coverslip was then removed and slides were washed 3 times in PBS to remove any mounting medium. Bleaching of the fluorochromes was achieved using a 1mg/mL solution of lithium borohydride in water (Acros Organics) for 15 minutes at RT. The slides were then washed 3 times in PBS prior to staining with a different set of antibodies as described above. The process was repeated a total of 5 times. Raw imaging data were processed using Imaris (Bitplane) using Hoechst as fiducial for the alignment of subsequent images. The staining set-up and antibody information is in Supplementary Table 5.
2) Regularization of within-experiment variation in RNA detection sensitivity , α  = 20 representing more lenient regularisation to account for large technical within-slide variability in total count observed in this data. The mode was trained until convergence was achieved (40,000 iterations). To aid interpretation, all locations in the Visium data were annotated with anatomical regions label using paired histology images. Each location on each Visium slide that was aligned with the tissue was annotated as "Tissue" (manual annotation) and was used for analysis. Specific tissue regions were annotated as follows: Cartilage, Glands, Multilayer epithelium, Parenchyma, Nerve bundle, Perichondrium, Airway smooth muscle, Arterial vessel, Venous vessel, Weird morphology, Mesothelium, Pulmonary vessel, Immune infiltrate (iBALT-like), Small airway (on parenchyma sections only).
The annotation was used to compute average estimated cell abundance of each cell type across annotated regions, which was normalised per cell type and presented as a dotplot. Single cell and nuclei RNA-seq analysis The CellRanger unfiltered matrices were used as an input for the SoupX algorithm (M. D. Young and Behjati 2020) to remove ambient RNA contamination. The single cell RNA-seq libraries were corrected according to the tutorial (https://github. com/constantAmateur/SoupX). For each single nuclei RNA-seq library, CellRanger filtered nuclei were first subjected to a set of QC filters. Pass QC nuclei were processed using standard scanpy pipeline and were clustered at a relatively low resolution to form 5-10 clusters. Nuclei that didn't pass QC were assigned to those clusters by logistic regression.
The rough level clustering was used to annotate master cell types. Master cell types were further subtracted and re-analysed with scanpy workflow including new highly variable genes detection. Typically, between 1000 and 3000 highly variable genes (HVG-s) were used for calculating 40 principal components that were used for calculating the UMAP coordinates. Data integration via Harmony  (Korsunsky et al. 2019)  or BBKNN  (Polański et al. 2020)  or scVI  (Lopez et al. 2018)  was used with either "Material" as input to correct between cells and nuclei, or "Material and Donor" in order to take into account the variability between donors. Clustering at different resolutions was done in order to determine biologically meaningful cell types. Doublets and contaminated cells were detected via overclustering and observing markers from more than one cell type, and in case of nuclei-seq also observing higher counts.
2020 ) and with CellChat (http://www. cellchat. org/). In order to cope with donor to donor differences, the dataset was downsampled to a set number of cells per every donor-celltype combination. Pseudotime analysis for selected cell populations was performed with Monocle3 (ref), its functionality to infer a pseudotime based on UMAP coordinates. The root was identified as the cell with the highest combined expression of canonical progenitor markers (VCAN for chondrocytes; TGM2, HMCN2 and SULF1 for smooth muscle). Cell trajectory analysis was performed using the scVelo package (v0. 2. 1)  (Bergen et al. 2020 ) and specifying stochastic model. Label transfer was performed via Azimuth tool (https://azimuth. hubmapconsortium. org/) with the lung reference data  (Travaglini et al. 2020). Human protein atlas (https://www.
proteinatlas. org/humanproteome/tissue) was used for extracting images of protein stainings with antibodies on human tissues. BCR and TCR analysis from VDJ-data VDJ analysis was done with Scirpy 0. 6. 0 (https://icbi-lab. github. io/scirpy/) (Sturm et al. 2020). For TCR data, clonotypes were defined based on CDR3 nucleotide sequence identity. For BCR data, clonotypes were defined based on the Hamming distance between CDR3 amino acid sequences with a cutoff of 2 and orphan VJ chains removed. In both cases, V gene identity was required and the CDR3 sequence similarity was evaluated across all of a cell pair's V(D)J chains.