dataesr/scientific-paragraphs-categorization huggingface dataset:
Gating strategy applied The CyTOF data analysis was performed according to the methods described by Orecchioni et al. 22  and Bendall et al. 49  Summarizing, normalized, background subtracted FCS files were uploaded into Cytobank for the analysis. Specific PBMC subsets and subpopulations were identified using the following strategy: T cells (CD45+ CD19-CD3+), T helper (CD45+ CD3+ CD4+), T cytotoxic (CD45+ CD3+ CD8+), T naive (CD45RA+ CD27+ CD38-HLADR-), T effector (CD45RA+ CD27-CD38-HLADR-), and activated (CD38+ HLADR+), B cells (CD45+ CD3-CD19+), B naive (HLADR+ CD27-), B memory (HLADR+ CD27+), plasma B (HLADR-CD38+), NK cells (CD45+ CD3-CD19-CD20-CD14-HLADR-CD38+ CD16+), Classical monocytes (CD45+ CD3-CD19-CD20-HLADR+ CD14+), Non classical monocytes (CD45+ CD3-CD19-CD20-HLADR-CD14-CD16+), mDC (CD45+ CD3-CD19-CD20-CD14-HLADR+ CD11c+ CD123-), and pDC (CD45+ CD3-CD19-CD20-CD14-HLADR+ CD11c-CD123+).
Cytobank allowed the realization of the heat map visualization comparing marker fluorescence of the treated populations with mean fluorescent intensity vs. the untreated control. viSNE tool was exploited. viSNE, a cytometry analysis tool implemented in Cytobank, uses t-stochastic neighbor embedding (t-SNE) portraying single cells in a two-or three-dimensional plot, on the basis of their relationships. Nine cell surface markers were used to draw the viSNE map for the Maxar PBMCs staining: CD3, CD4, CD8a, CD11c, CD14, CD16, CD19, CD20, CD123, and HLADR. The specific viSNE on B cells populations were performed on the gated CD3-CD19/CD20+ events; to construct the viSNE map CD27, CD38, and HLADR were applied. The cytokine data analysis was performed exploiting viSNE tool. Heat maps and plots report the median signal intensity (MSI) of the analyzed cytokines (IL-2, IL-4, IL-5, IL-6, MIP1, TNF and GrB).
DNA fragmentation assay For DNA fragmentation assay, PBMCs were seeded into 6 well plates (0. 5-1×10 6 cells per well). After seeding, the cells were treated with GO or GONH2 and incubated at 37 °C and 5% for 24 h. As positive control, cells were treated with 30% DMSO. After 24 h, cells were collected in 1. 5 ml microcentrifuge tubes and centrifuged at 200×g for 10 min. Cell pellets were washed with PBS and centrifuged again at 200×g for 5 min. After washing, the pellets were used in the DNA Ladder Detection Kit (Abcam), according to the kit protocol. At the end, samples were run in 1. 8% agarose gel for 75 V for 2 h. After running, the gel was stained according to the kit protocol and visualized with the Gel Doc EZ System (BIO-RAD) and gel images were analyzed in ImageJ software.
DNA damage assessment In order to determine DNA damage and repair levels, cells (1×10 6 cells) were collected and washed two times by Dulbeccos Phosphate Bufered Saline (DPBS) (Life Technologies) Later, cells were permeabilized with Tween20 containing PBS which was followed by 1 h incubation with antiphospho H2AX antibody in 1% bovine serum albumin containing PBS. Prepared samples were analyzed with BD Accuri Plus flow cytometer. Data were analyzed with BD Accuri Plus software.
Gene expression impact of GOs on B cells Total RNA was extracted and purified from B cells (Raji (B cell line) using the RNAeasy mini kit (Qiagen, Valencia, CA, USA). RNA purity was assessed by spectrophotometric analysis and integrity by microfluidic molecular sizing using the Bioanalyzer 2100 (Agilent). An RNA Integrity Number (RIN) >/= than eight was required. RNA (1 μg) was converted in cRNA and labeled (Illumina totalPrep RNA amplification kit, Ambion). Biotinylated cRNA was hybridized onto the Illumina HumanHT-12 v4 (Illumina, Inc. , San Diego, CA, USA). The Illumina HumanHT-12 v4 interrogates the expression of 47323 probes derived from the National Center for Biotechnology Information Reference Sequence RefSeq Release 38 and other sources. Probe intensity and gene expression data were generated using the Illumina GenomeStudio software V2011.
1 (Gene Expression Module V1. 9. 0). Pre-normalization expression data was read into R using the lumiR function from the "lumi" package (v2. 34). 50 Probe to gene annotation was added using the lumiHumanAll. db package (v1. 22), and non-matching probes were discarded. Expression was quantile normalized and log2 transformed using lumiN and lumiT functions, respectively. Flat probes were dropped, and the data was saved and a Rdata object containing the lumi expression sets (pre and post normalization) and the probe expression matrix along with sample annotation. Probes targeting the same gene were averaged and a final matrix of 14, 595 unique genes was obtained. The matrix used for the analysis is available as Electronic Supplementary Information. Differential gene expression analysis was performed using limma (linear model for microarray data) (v3.
38). 51 lcano plots of differential expressed genes were plotted using the EnhancedVolcano package (v1. 0. 1) Differentially expressed genes (DEGs) were defined using a nominal p value < 0. 005 and a false discovery rate (FDR) p value < 0. 1 using the Benjamini-Hochberg method. 52  These lists of genes were used for pathway analysis. Enriched pathways were plotted using ggplot2. (v3. 2. 1). For pathway analysis, we used to orthogonal methods. First, we used Ingenuity Pathway Analysis (IPA) (www. qiagen. com). IPA is closed-source software containing curated biological pathways called canonical pathways. In addition to IPA, to increase robustness of the analysis, we implemented a systematic pathway analysis method (called here RMDB) based on open-source databases. First, we determined enriched pathways using ConsensusPathDB.
47, 53   onsensusPathDB integrates interaction networks of humans including complex protein-protein, genetic, metabolic, signaling, gene regulatory and drugtarget interactions from 32 public resources including KEGG,  54, 55  BioCarta,  55  PID, Reactome,  56  WikiPathways 57 etc. into a unique interaction network with complementary non-redundant type of edges. The advantage of using ConsensusPathDB over a popular tool like DAVID  58  is that it provides the option to search through multiple databases (different types of interactions) to find enriched pathways unlike DAVID which uses only the KEGG database. Moreover, unlike IPA, Consensus PathDB is a free open source software available for such enrichment analysis. However, one of the issues with Consensus PathDB is that the same enriched pathway might appear from multiple different databases.
Acknowledgements This work was partly supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement no. 734381  ( CARBO-IMmap ) and  FLAGERA JTC Graphene 2015 (G-IMMUNOMICS  project), and the  Agence Nationale de la Recherche (ANR)  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ). The authors wish to thank  C. Royer  and  V. Demais  for TEM analyses at the  Plateforme Imagerie in Vitro at the INCI (Strasbourg, France ),  F. Rodrigues  for AFM analysis and L. Newman for Raman measurements.
Clustering We used the clustering algorithm called hierarchical densitybased spatial clustering of applications with noise (HDB-SCAN)  (Campello et al. 2013)  in its python implementation  (McInnes et al. 2017 ) to perform our membership study. It aims at improving the performance of the widely used density-based algorithm DBSCAN  (Ester et al. 1996) , which was successfully applied to the search of OCs by  Castro-Ginard et al. (2018 , 2019 , 2020). One of the main advantages of HDBSCAN over DBSCAN is that it is able to detect overdensities of varying density in a dataset. To do this, HDBSCAN adds a hierarchical approach to DBSCAN. To detect a cluster, DBSCAN draws hyperspheres of radii around each star and considers as a cluster the points inside a hypersphere that contain more than minPts.
When the hierarchical tree of the dataset has been built, HDBSCAN offers two options to select the clusters: the excess of mass (EoM) clustering, and the leaf clustering. We selected the clusters according to the leaf method, which chooses the clusters located at the lowest level of the tree. As noted by  Hunt & Reffert (2021) , the leaf clustering method almost always performed better in the identification of OCs, and we therefore adopted this method. We ran HDBSCAN on each dataset resulting from the Gaia EDR3 query described in Sect. 2. 1. Following  Kounkel & Covey (2019) , we chose as initial parameters min_cluster_size=40 and min_samples=25. We tested this analysis with different input parameters on a subset of clusters that was representative of our sample and found this choice to be the best compromise.
Fitting procedure To remove projection effets due to clusters located at high Galactic latitudes and to members located far from the cluster centers, we used Eq. (  2 ) to project the Galactic latitude and longitude of each star in a cluster on a plane tangential to the celestial sphere. In order to fit a GMM on these coordinates, we used a variational GMM with a Dirichlet process prior 3. This algorithm is a variant of the classical GMM and allows inferring the 3 Algorithm implemented in the scikit-learn python package  (Pedregosa et al. 2011). effective number of components from the data. Usually, classical GMM fitting takes advantage of the expectation maximization (EM) algorithm. In the EM algorithm, the parameters of the Gaussians are randomly initialized (the user can also provide a first guess), and the algorithm computes the probability with which each data point belongs to each component.
Method In order to measure the degree of mass segregation, we applied the method proposed by  Allison et al. (2009a)    Román  -Zúñiga et al. 2019). This method works by comparing the length of the minimum spanning tree (MST) of the most massive stars of a cluster with the length of the MST of a set of the same number of randomly chosen stars. An MST of a set of points is the path connecting all the points, with the shortest possible path length and without any closed loops. In a given set of points, only one MST can be drawn. We computed the MST by using the csgraph routine implemented in the scipy python module  (Virtanen et al. 2020). In all the cases, we drew the MST in the same set of coordinates as defined in Eq. (  2 ). The masssegregation ratio (MSR) Λ MSR is then defined as follows: Λ MSR (N) = l random l massive ± σ random l massive , (4) with l random being the average length of the MST of N randomly chosen stars and l massive the length of the MST of the N most massive stars.
Conclusion We developed a method that can identify members in the peripheral area of OCs up to 50 pc from their center. The method is based on the unsupervised clustering algorithm HDB-SCAN, which can detect overdensities in the astrometric space (µ α * , µ δ , ) even in datasets with varying density. We applied this method on 467 OCs from CAN+20 that are located closer than 1. 5 kpc and are older than 50 Myr. We report memberships for 389 OCs. The 78 remaining clusters are too embedded in their field for our method to properly distinguish them from their neighbors. For the vast majority of clusters, we identify many more members than were known previously. For COIN-Gaia 13, a very extended cluster, we tried to increase our search radius and recovered members up to 150 pc from the cluster centers.
Acknowledgements. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (http://www. cosmos. esa. int/Gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https:// www. cosmos. esa. int/web/Gaia/dpac/consortium). We acknowledge the  Gaia Project Scientist Support Team  and the  Gaia DPAC. Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This research made extensive use of the SIMBAD database, and the VizieR catalog access tool, operated at the  CDS, Strasbourg, France , and of  NASA Astrophysics Data System Bibliographic Services. This research has made use of Astropy (Astropy Collaboration 2013),  Topcat (Taylor 2005 ). Y. T. , C. S. , and L. C.
acknowledge support from " programme national de physique stellaire" (PNPS) and from the "programme national cosmologie et galaxies" (PNCG)  of  CNRS/INSU. L. C. acknowledges the support of the postdoc fellowship from  French Centre National d'Etudes Spatiales (CNES). This work was (partially) supported by the  Spanish Ministry of Science, Innovation and University (MICIU/FEDER, UE)  through grant  RTI2018-095076-B-C21 , and the  Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia 'María de Maeztu ') through grant  CEX2019-000918-M. J. O. acknowledges financial support from: (i) from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  682903 ,  P. I. H. Bouy ), (ii) the  French State in the framework of the "Investments for the future" Program ,  IdEx Bordeaux , reference  ANR-10-IDEX-03-02 , and iii) the  Agencia Estatal de Investigación of the Ministerio de Ciencia, Innovación y Universidades  through project  PID2019-109522GB-C53.
Non-breeding flock data Data were collected between 1 September 2013 and 6 March 2014. All observations were made by specialists on the species with long experience in counting, sexing, and ageing Little Bustards in flocks. We used a visual counting and mapping method  (Bibby et al. 2000)  adapted for a large area to locate the flocks (García de la  Morena et al. 2007). Surveys consisted of car routes using the existing rural track and road network; stops were made every 500-1,000 m, usually at high-visibility points, to scan visually the area looking for Little Bustard flocks. Each site was surveyed by a team of one to five observers provided with 8-10 Â 40 binoculars and 20-40 Â 80 telescopes (see Garcia de la Morena et al. 2007 for more survey details). Flocks were examined ad libitum (i.
Data treatment and analyses A total of 86 flock observations from the seven study areas was obtained, from which we calculated three ASR estimates. A first estimate of ASR for each flock was calculated as the proportion of adult individuals that were identified as females (Minimum female ratio, MinFR = females / [males þ females]). We also estimated a non-conservative proportion including individuals likely to be adult females in each flock as [females þ female-like individuals] / [females þ female-like individuals þ males]. This estimate can be contemplated as a Maximum adult female ratio (MaxFR) because it considers all female-like individuals to be adult females (something unlikely, as some of those must be juvenile males). Neither of those two calculations include unidentified individuals, the proportion of which varied among areas (see Table  1 ) in relation to visibility (average distance at which flocks were assessed, topography, or vegetation) or time of counting (from December onwards discrimination between young birds and adult females is not possible).
Acknowledgements. This paper is a contribution to the REMEDINAL 3 ( S2013/MAE-2719 ) network which funded a post-doc contract for ESD. It also contributes to the  Excellence Network REMEDINAL 3CM  ( S2013/ MAE2719 ), supported by  Comunidad de Madrid. We thank all the field workers that collaborated in this study. Thanks to  Ricardo Montero  from  Extremadura Birding  who provided us with a Little Bustard winter flock video record. We are grateful to  Julia Gómez-Catasús  for her help with the bootstrapping analysis. This study was carried out with no funds from the  Spanish Ministry of Science and Innovation. Supplementary Materials. To view supplementary material for this article, please visit http://doi. org/10. 1017/S0959270922000430.
Regularly acquired synthetic aperture radar (SAR) images, for example those acquired by the European Space Agency Sentinel-1 constellation, represent a new opportunity to obtain landslide timing information for many landslides at a regional scale. SAR images penetrate cloud cover, and the Sentinel-1 satellites acquire images every 12 d on two tracks over all land masses globally. Numerous studies have demonstrated that SAR data can be used to detect the spatial distribution of landslides in the case where their timing is already known, for example in the case of earthquake-triggered landslides where it can be assumed that the landslides occurred concurrently with ground shaking  (Aimaiti et al. , 2019; Bur-rows et al. , 2019 Bur-rows et al. , , 2020;; Ge et al. , 2019; Konishi and Suga, 2019; Masato et al.
, 2020; Mondini et al. , 2019; Yun et al. , 2015). SAR can be also used to monitor movements of slowmoving landslides (e. g. Ao et al. , 2020; Bekaert et al. , 2020; Hu et al. , 2019; Kang et al. , 2021; Solari et al. , 2020). Mondini et al. (2019)  used SAR to establish the timing of a single large landslide. However, to date SAR has not been used to refine timing estimates of landslide inventories. Here we present landslide timing methods based on the Sentinel-1 SAR dataset in Google Earth Engine that represent a step towards this goal of improved landslide inventory temporal resolution and could unlock new comparisons between measured or modelled hydrological time series and landslide occurrence.
It is thus important to test landslide timing methods in this environment, but inventories of rainfall-triggered landslides of known timing are not available. Therefore we instead used earthquake-triggered landslides. Since the inventory of  Roback et al. (2017)  covers a large area (28 000 km 2 ), with different areas having different Sentinel-1 coverage, we focussed on triggered landslides within three valleys: Trishuli, Bhote Kosi and Buri Gandaki. These valleys experience large numbers of rainfall-triggered landslides every year  (Marc et al. , 2019a). All inventories were filtered to remove landslides smaller than 2000 m 2. Since the Sentinel-1 Ground Range Detected (GRD) dataset has a pixel size of 10 × 10 m, this should result in a minimum of 20 SAR pixels within each landslide. This resulted in inventories of 543 landslides for the Hiroshima event and 383 for Zimbabwe.
SAR data and preprocessing To construct our SAR amplitude time series, we used the Google Earth Engine Sentinel-1 GRD dataset. Google Earth Engine is a freely accessible, cloud-based platform that allows users to access Sentinel-1 data without the technical expertise and computational facilities otherwise required to process SAR data. It also provides access to other datasets used in this study, such as Sentinel-2 and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The Sentinel-1 GRD data are preprocessed following the workflow of  Filipponi (2019)  to obtain the backscatter coefficient σ 0 at a resolution of 20 × 22 m in radar coordinates. The data are then resampled onto a 10 m grid in projected coordinates. We then applied the module of Vollrath et al. (  2020 ) using the 30 m SRTM DEM to carry out an angular radiometric slope correction based on the volume scattering model of  Hoekman and Reiche (2015).
Our method is designed to detect only a single failure. In the case where multi-stage failure results in more than one step change in the time series, https://doi. org/10. 5194/nhess-22-2637-2022 Nat. Hazards Earth Syst. Sci. , 22, 2637-2653, 2022 Figure  6. The distribution of total landslides (white), landslides assigned a time (light green) and landslides assigned the correct time (dark green) over aspect for each technique using ascending and descending track SAR over the Zimbabwe dataset. the convolution in Sect. 2. 5 will detect only the largest step change. Though it is beyond the scope of this study, in theory it would be possible to assess if the time series contain a second peak of similar magnitude to the largest one in order to assess possible multi-stage failure or landslide reactivation.
Coherence surfaces and the phase data required for their calculation are not available through Google Earth Engine. However, coherence for Track 19 in Nepal has previously been calculated by  Burrows et al. (2019) , covering the Buri Gandaki and Trishuli inventories tested here. This allows us to compare techniques of landslide timing based on SAR amplitude and InSAR coherence for these two case studies. Burrows et al. (2019)  processed the data at the same resolution as that used here (20 × 22 m) and used a 3 × 3 moving window to estimate coherence, so the coherence surface has a resolution of 60 × 66 m. Similarly to the landslidebackground technique, we obtained the median coherence of pixels within each landslide through time and the median coherence of pixels within a 60-500 m buffer of each landslide polygon to give a background coherence.
Conclusions and future perspectives In the case of long or successive rainfall events, landslide inventories compiled from optical satellite imagery are often poorly constrained in time, making it difficult to associate them with specific triggering conditions. Here we present a method of using Sentinel-1 SAR amplitude time series in Google Earth Engine to identify the timing of triggered landslides to within a few days. We find that by combining multiple techniques and ascending and descending track SAR, it is possible to assign timings to up to 30 % of landslides in an inventory with an accuracy of 80 %. A small number of landslides (5 %-10 %) can be timed with an accuracy of 90 %. Here we applied our method to optically derived landslide inventories, but it could also be applied to datasets from other sources, for example those based on lidar scans or high-resolution optical images that allow landslide volumes to be estimated  (Bernard et al.
Second, remote sensing approaches such as we present here could be combined, where available, with other methods of establishing landslide timing, for example reports of individual landslides or seismic data  (Bell et al. , 2021; Hibert et al. , 2019; Yamada et al. , 2012). Finally, we also expect that both the precision and the number of landslides that can be assigned timings may increase in the future as more SAR data become available, for example from the planned NISAR constellation. Overall, our method represents a step towards improved temporal resolution for triggered landslide inventories. This could further our understanding of monsooninduced landsliding in the Nepal Himalaya and elsewhere.
Acknowledgements. We thank  Robert Emberson  for sharing some landslide inventories. Financial support. This research has been supported by the  Centre National d'Etudes Spatiales  (post-doctoral fellowship -Characterising the temporal evolution of rainfall-triggered landslides using radar and optical satellite data). Review statement. This paper was edited by  Ugur Ozturk  and reviewed by  Magdalena Stefanova Vassileva  and three anonymous referees.
Code and data availability. Sentinel-1 GRD, Sentinel-2 and Landsat imagery were accessed here through Google Earth Engine. Sentinel-1 and Sentinel-2 data are provided with open access by ESA Copernicus (https://scihub. copernicus. eu/dhus/#/home, Copernicus, 2022). Landsat imagery is provided by the US Geological Survey (https://earthexplorer. usgs. gov/, U. S Geological Survey, 2022). Landslide polygons were obtained from Roback et al. (2017) for Nepal (available at https://doi. org/10. 5066/F7DZ06F9), The Association of Japanese Geographers (2019) (http://ajg-disaster. blogspot. com/2018/07/3077. html) for Hiroshima and Emberson et al. (2022) (https://doi. org/10. 5194/nhess-22-1129-2022) for Zimbabwe. Google Earth Engine and Python codes developed as part of this paper are available on GitHub at https://github.
com/ KABurrows/Supplement-to-nhess-2022-21 (last access: July 2022) and https://doi. org/10. 5281/zenodo. 6984291 (Burrows, 2022). Images were produced using Python Matplotlib  (Hunter, 2007)  (https://doi. org/10. 1109/MCSE. 2007. 55) and PyGMT  (Uieda et al. , 2021)  (https://doi. org/10. 5281/zenodo. 5607255) software. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/nhess-22-2637-2022-supplement.