dataesr/scientific-paragraphs-categorization huggingface dataset:
The following steps were done with Drop-seq tool (v 2. 0. 0). Trimming of 5' adapter sequences and of polyA tails was performed. Unaligned BAM was transformed to fastq with the Picard tool, prior to alignment with STAR on mm10 reference genome. Ddseeker bam outputs previously tagged with molecular/cell barcode were merged with aligned BAM files, according to the Drop-seq tool cookbook. Finally, TagReadWithGeneExonFunction was used to annotate each read with the gene it belongs to, and DigitalExpression was used to count gene transcripts in each cell. The output DGE matrix file is a matrix with a row for each gene, and a column for each cell, containing the number of transcripts observed. This output was loaded into the Seurat (v2) R package for further analysis keeping only cells where at least 200 features were detected, and genes detected in at least 5 cells.
The final dataset contains 1740 cells and 15,448 genes. To normalize the data, we applied the global-scaling normalization method "LogNormalize" which normalizes the feature expression measurements for each cell by the total expression, multiplies this by a scale factor (10,000 by default), and log-transforms the result. Then, highly variable genes were detected prior to scaling transformation. To cluster cells, we computed a principal components analysis (PCA) on scaled variable genes, as determined above, using Seurat's RunPCA function, and visualized it by computing a Uniform Manifold Approximation and Projection (UMAP) using Seurat's RunUMAP function on the top 10 PCs. We used FindClusters function with a resolution of 0. 6 resulting in 8 clusters. TME and malignant cluster cells were identified according to the expression of the GFP transgene.
Single-cell RNA-seq and analysis-10X data Cells suspension of four dissociated GBMs (2 WT+GCV and 2 p16-3MR +GCV) were loaded with the Chromium Next GEM Chip G Single Cell Kit (10X Genomics, #PN-1000120) and a library was generated using Chromium Next GEM Single Cell 3ʹ Reagent Kits v3. 1 (10X Genomics, #20012850). The library was sequenced on an Illumina NovaSeq 6000 instrument using a 100-cycle S2 flow cell in XP mode, with the following parameters: 2050 million reads depth, 200 Gbases per run, and 50,000 reads per cell. The Cell Ranger Single-cell Software suite (v. 3. 0. 2) was used to process the data. First, a custom reference genome was created with the mkref function to include 3'LTR and 3MR sequences into the mm10 reference genome. Count function was used on each GEM well that was demultiplexed by mkfastq to generate gene-cell matrices.
Then, filtered_feature_bc_matrix output was loaded into the Seurat Bioconductor package (v. 3. 2. 3) to filter the datasets and identify cell types using R (v. 3. 6). Genes expressed in at least five cells and cells with at least 200 features were retained for further analysis. To remove likely dead or multiplet cells from downstream analyses, cells were discarded when they had <500 unique molecular identifiers (UMIs), >60,000 UMIs, or expressed over 8% mitochondrial genes. All samples were merged together for downstream analysis. As no batch effects were observed among the four samples, no integration step was performed. Gene expression matrix was normalized using the negative binomial regression method implemented in the Seurat SCTransform function, via the selection of the top 3000 variable genes and regressed out the mitochondrion expression percentage.
The final dataset was composed of 20,293 genes and 26,237 cells. To cluster cells, we computed a principal components analysis (PCA) on scaled variable genes, as determined above, using Seurat's RunPCA function, and visualized it by computing a Uniform Manifold Approximation and Projection (UMAP) using Seurat's RunUMAP function on the top 30 PCs. We also computed the k-nearest neighbor graph on the top 30 PCs, using Seurat's FindNeighbors function with default parameters, and in turn, used Seurat's FindClusters function with varying resolution values. We chose a final value of 0. 5 for the resolution parameter at this stage of clustering. Clusters were assigned preliminary identities based on the expression of combinations of known marker genes for major cell types. TME clusters were identified with the expression of the Ptprc (Cd45) gene marker.
In order to better identify other cell types, TME cells were removed and a second clustering with a resolution of 0. 6 was applied. The FindMarkers function with the default parameters (min. -LogFC = 0. 25, min. pct = 0. 25, test. use = Wilcox) was used to identify differentially expressed genes in different conditions: (i) p16-3MR +GCV vs. WT+GCV in each cluster; (ii) cells from astrocyte and NP clusters with Cdkn2a expression ≥4 (307 cells) vs all the other cells (10,280 cells) in WT+GCV GBMs. Functional enrichment analysis was done with clusterProfiler (v3. 14. 3) Bioconductor package on the differentially deregulated genes with over-representation analysis (enricher function) and with Gene Set Enrichment Analysis (GSEA function 100 ) on all the genes. We searched for ligand/receptor interactions between cluster 0 et Cd45 positive clusters at 0.
Signature expression analyses We analyzed the senescence signature through our tumoral SCT normalized dataset and three datasets corresponding to patient GBMs  23, 26, 54. The Neftel et al. , dataset was processed via Seurat (v3. 2. 3), 10X samples were normalized via the SCT method and Smartseq2 samples were retrieved in log2(TPM + 1). Fastq files for the Bhaduri et al. , 10X dataset were processed by cellranger, followed by Seurat analysis with the SCT method. Finally, we retrieved the normalized expression matrix of Johnson and colleagues via synapse (https://www. synapse. org/#!Synapse:syn22257780/wiki/604645). For both murine and patient GBM datasets, we filtered out transcriptomes expressing the CD45 (PTPRC) gene and pediatric GBMs  23  and we calculated a senescence score resulting from the singlesample GSEA 102 using the R package GSVA (v.
Cox regression analysis Normalized intensities from TCGA microarray data were obtained from cBioPortal (cbioportal. org), filtering for GBM TCGA, Firehose Legacy dataset. First, single-sample GSEA scores were calculated with R package GSVA (v1. 32. 0) for senescence genes signature and NRF2 targets signature. Secondly, we fitted a Cox proportional hazards regression model with the coxph function from the survival R package (v2. 44-1. 1), with additional covariates such as p16 copy number alteration (CNA) status, and age of patients. Plots were done with ggforest R function.
Acknowledgements The authors acknowledge the service of the ICM platforms: iGenSeq, iVector especially  Phillippe Ravassard , icm-QUAN-Imaging,  Pheno-ICMice , Histomics-Histology, Celis-Cell culture, the Data Analysis Core platform for their expertise in statistics, in particular,  François-Xavier Lejeune  and the service of the animal facility  UMS28 (Sorbonne University ) and the service of the flow cytometry platform CYTO-ICAN especially Florence Deknuydt ( Hôpital Pitié-Salpétrière). The authors are grateful to  Judith Campisi ,  Inder Verma ,  Magdalena Götz ,  Anton Berns , and the  Centro Nacional de Investigaciones Oncológicas (CNIO)  for providing reagents and  Carol Schuurmans  for comments on the manuscript. We also thank members of the  Huillard-Sanson laboratory  for discussions, and help with the experiments, in particular  Sophie Paris  for teaching the intracranial injection.
Data availability The mm10 reference genome was retrieved from https://hgdownload. cse. ucsc. edu/goldenpath/mm10/chromosomes/. Gene Set Enrichment analysis gene sets came from MSigDB collections (https://software. broadinstitute. org/cancer/software/gsea/wiki/index. php/MSigDB_ collections). The Neftel et al. , the dataset was retrieved via the single cell portal (singlecell. broadinstitute. org), fastq files for the Bhaduri et al. , dataset, were retrieved from SRA bioproject PRJNA579593, and the normalized expression matrix of Johnson and colleagues was retrieved via synapse (https://www. synapse. org/#!Synapse:syn22257780/wiki/ 604645). Normalized intensities from TCGA microarray data were obtained from cBioPortal (cbioportal. org), filtering for GBM TCGA, Firehose Legacy dataset. The raw data generated in this study are provided in a Source data file and a Source supplementary data file.
Our understanding of the present-day state and evolution of the Canadian and Alaskan mantle is hindered by a lack of absolute P-wavespeed constraints that provide complementary sensitivity to composition in conjunction with existing S-wavespeed models. Consequently, cratonic modification, orogenic history of western North America and complexities within the Alaskan Proto-Pacific subduction system remain enigmatic. One challenge concerns the difficulties in extracting absolute arrival-time measurements from often-noisy data recorded by temporary seismograph networks required to fill gaps in continental and global databases. Using the Absolute Arrival-time Recovery Method (AARM), we extract >180,000 new absolute arrival-time residuals from seismograph stations across Canada and Alaska and combine these data with USArray and global arrivaltime data from the contiguous US and Alaska.
Absolute Arrival-Time Residuals To constrain absolute P-wavespeeds throughout the mantle at continental scales, global and regional absolute arrival-time data sets are required to resolve both subducted slabs (e. g. ,  Sigloch, 2011)  and lithospheric boundaries (e. g. ,  Boyce et al. , 2019). We use the global "EHB" database  (Engdahl et al. , 1998 ) recorded from 1964 to 2004 (e. g. , C. Li, Van der Hilst, et al. , 2008)  and USArray Transportable Array (TA) data recorded from 2004 to 2020 (updated from  Astiz et al. , 2014; Burdick et al. , 2017)  providing absolute arrival-time picks for phases P, Pn, Pg, pP, PKP, and PKIKP (see Figures  S1-S3  in Supporting Information S1). Canadian station distribution within the global database is sparse and a great number of networks in Alaska are rarely used, so we supplement EHB and TA data with other temporary networks in Canada and Alaska.
Data from station networks active during the period 2005-2020 were accessed via IRIS and the Canadian National Data Center databases. We exclude stations associated with glacial monitoring to avoid possible travel-time inaccuracies. Seismic data are extracted from the following specific networks, with more details available in the Open Research Section and Supporting Information S1:  AK-1987 (Alaska Earthquake Center, Univ. of Alaska Fairbanks, 1987; D'Alessandro & Ruppert, 2012) ,  AT-1967 (NOAA National Oceanic and Atmospheric Administration (USA), 1967; Oppenheimer et al. , 2005) ,  AV-1988 (Alaska Volcano Observatory/USGS, 1988; Dixon et al. , 2013) ,  CN-1975 (Natural Resources Canada (NRCAN Canada), 1975; North, 1994; Bent et al. , 2019) ,  C8-2002 (Natural Resources Canada (NRCAN Canada), 2002) , EO-2018 (University of Calgary (U of C Canada), 2018;  Boggs et al.
, 2020; Liddell et al. , 2018). We calculate relative arrival-times separately within each region using Multi-Channel Cross-correlation  (VanDecar & Crosson, 1990 ) for all sub-regions excluding NWC and HSB. For NWC we repick the relative arrival-times of  Estève et al. (2020)  spanning 2002-2018 but exclude the USArray TA data (because we take these picks from the TA data set), to ensure compatibility with our processing. We take directly the relative arrival-times of  Liddell et al. (2018) , calculated following  Rawlinson and Kennett (2004) , spanning 2004-2015 into step two. In step two, we apply the AARM  (Boyce et al. , 2017)  to each sub-region data set to obtain absolute arrival-time picks compatible with EHB and TA databases. In this approach, we optimally stack the unfiltered raw data based on alignments derived from the relative arrival-time step to reveal the first-break or onset time of the P-wave above the noise on the resulting high SNR stack.
75 s), in agreement with Y. Chen et al. (2018). A marked change in residuals is observed approximately coincident with the LLTZ and GSLsz in northwest Canada. North of this boundary, delayed arrivals are observed both west of the CDF below the Cordillera and also continuing beyond the CDF to the east through the Proterozoic orogens (up to 1. 75 s delay), toward the Slave craton (∼0. 75 s delay). By contrast, residuals below the northern Mackenzie Mountains are of opposite sign (-1. 0-0 s). The TA data set reveals low amplitude residuals throughout western Yukon, northern and central Alaska. In southern Alaska and along the Aleutian Arc, residuals are often earlier than expected (∼1. 5 s).
Canada Figure  13  compares CAP22 to four global P-wave models that capture wavespeed variation throughout North America: PRI-P05  (Montelli et al. , 2006) , MIT-P08 (C. Li, Van der Hilst, et al. , 2008) , LLNL-G3Dv3  (Simmons et al. , 2012) , DETOX-P2  (Hosseini et al. , 2019). Within the contiguous US upper mantle, although amplitudes differ, the pattern of wavespeed variation is broadly similar across all models. Specifically, the location of the slow-to-fast wavespeed transition separating the active western US from the stable Precambrian interior shows good agreement. However, in Canada, substantial differences exist. Previous models do not show slow P-wavespeeds east of the CDF and beneath the Slave craton in northwest Canada, likely due to limited station coverage. Despite data differences, at ≥1,000 km depth CAP22 is largely similar to previous models (Figure  13 ).
3%, 100-300 km depth) northeast of the Yukon-NWT border and CDF (MC: Figures  6  and 11 ). Based on a very similar station distribution to CAP22,  Estève et al. (2020)  used relative arrival-time data to image high P-and S-wavespeeds below the proposed Mackenzie craton, with elevated wavespeeds continuing west of the CDF. Surface wave tomography  (Schaeffer & Lebedev, 2014) , capable of resolving wavespeed variations between stations, shows fast wavespeeds east of the CDF in this region, which provides strong evidence in favor of the Mackenzie craton. Other geophysical observables such as the strong, deep magnetic high imaged in this region  (Saltus & Hudson, 2007) , provide further evidence for the presence of the Mackenzie craton. Despite limited station coverage, CAP22 provides reasonable evidence for a Mackenzie craton, although new station deployments are required in the region to confirm its lateral extent using body waves.
By contrast, slow wavespeeds below the northern CCD continue northeastward of the Mackenzie Mountains beyond the CDF into the central Slave craton. This north-south change in wavespeed pattern is colocated with the change in geomorphic expression along the CCD, and importantly the LLTZ, implying that such transfer zones are not limited to crustal depths. In northwest Canada, magnetic anomaly data resolve high-amplitude magnetic highs bounding the Mackenzie Mountains to the north and south  (Saltus & Hudson, 2007) , interpreted as mafic lower crust and underlying depleted upper mantle. These inferred strong lithospheric blocks may provide buttresses to orogenic deformation and allow the CDF to develop its arcuate shape by broadening the northern CCD relative to its southern counterpart. Cordilleran deformation proceeded further eastward here because the Precambrian lithosphere to the east has been weakened through metasomatic modification (Section 4.
Data Availability Statement Phase arrivals from the EHB Bulletin are available at http://www. isc. ac. uk/ehbbulletin/. Phase arrivals from USArray are available to the community as CSS monthly files from the ANF (http://anf. ucsd. edu/tools/ events/). Seismic data was obtained (last accessed 06 Jul 2022) from the IRIS (https://ds. iris. edu/ds/nodes/ dmc/) database and Canadian National Data Centre (https://earthquakescanada. nrcan. gc. ca/stndon/CNDC/ index-en. php). The waveform data used in each sub-region of this study during data processing are from the following networks: ALO: XO-2018  (Abers et al. , 2018; Barcheck et al. , 2020). ALS: AK-1987 (Alaska Earthquake  Center, Univ. of Alaska Fairbanks, 1987; D'Alessandro & Ruppert, 2012), AT-1967 (NOAA National Oceanic  and Atmospheric Administration (USA), 1967; Oppenheimer et al.
Acknowledgments  C. Tebaldi  was supported by the  Calibrated and Systematic Characterization, Attribution and Detection of Extremes (CASCADE)  project, funded by  US Department of Energy, Office of Science, Office of Biological and Environmental Research  under Contract No. DE-AC02-05CH11231  and by the  Integrated Coastal Modeling (IcoM)  project, also funded by  US Department of Energy, Office of Science, Office of Biological and Environmental Research. A. C. Ruane received support from  US NASA Earth Sciences Division Climate Impacts Group funding and Community Service  grants. REK was supported by  NASA  (award  80NSSC20K1724  and JPL task  105393. 509496. 02. 08. 13. 31 ) and by  US National Science Foundation  award ICER-2103754, as part of the  Megalopolitan Coastal Transformation Hub.
T. L. Edwards was supported by the  UK Natural Environment Research Council  ( NE/T007443/1 ) and by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 869304 , PROTECT contribution number TBD. JS is supported by the  Cluster of Excellence Climate, Climatic Change and Society (CLICCS)  funded by  DFG  and the  CICERO Strategic Project on Climate Change Risk  (no. 160015/F40 ), funded by the  Research Council of Norway, Norway. PZ was supported by the " National Network on Climate Change and its Impacts-CLIMPACT " funded by the  Public Investment Program of Greece  (grant no. 2018SE01300001 ). The authors would like to thank the editor and two anonymous reviewers for their feedback, which substantially improved this article.
Software and algorithms Fiji microscope stand equipped with a motorized stage, a piezo Z-stage (Mad City Labs), a focus stabilization system (Perfect Focus System, Nikon) and a 100X, NA 1. 49 oil objective (CFI SR HP Apochromat TIRF 100XC, Nikon). An Abbelight SAFe360 module is attached to the side C-mount of the stand. It receives laser excitation from an Oxxius L4Cc Combiner equipped with 640 nm (500 mW), 532 nm (400 mW), 488 nm (150 mW) and 405 nm (100 mW) lasers through the ASTER scanning module (Abbelight). 37  Separation between excitation and emission bands is done using a quad-band dichroic mirror (Semrock, Di03-R405/488/532/ 635-t3-25x36), and emission light is further separated using a dichroic mirror (662 nm Semrock FF662-FDi02-t3-25x36 or 700 nm FF699-FDi01-t3-25x36), filtered on both paths by quad-band emission filters (Semrock FF01-446/510/581/703-25) and captured by two Hamamatsu Photonics Flash4 V3 sCMOS cameras.
SMLM data processing Acquired PAINT and STORM sequences were processed using the Abbelight Neo Analysis software. Detection of intensity peaks used a wavelet algorithm  66  after local means background estimation and removal. Intensity peaks at least twice as high as the background with a size of 3x3 to 7x7 pixels area (300-700 nm) were considered a single-molecule blinking event and further processed for fitting using Gaussian fitting with least-squares error. The number of photons emitted by the blinking event was estimated from the background-subtracted raw data by integration over a 11x11 pixel round area (1. 1 mm diameter). 67  Frame sequences from each camera were processed individually to generate two lists of localizations containing their coordinates and photon number. Three-dimensional astigmatism-based acquisition were fitted for Z position according to the eccentricity of the PSF using a calibration obtained on 100 nm beads.
Channel assignment (demixing) from two-camera data After fitting, two images were reconstructed using the localizations list from each camera and were then aligned using an affine transform to correct for lateral aberrations (OpenCV, ''Motion_affine'', correction for rotation, translation, scale and shear). The correction is illustrated in Figure  S5. The localization coordinates obtained from each camera image sequence were then modified using the determined affine transformation, before checking for pairs of localizations between the two cameras: localizations appearing at the same coordinates with a tolerance of 500 nm on identical frames. For each detected pair, the ratio of the emitted photons was calculated with: R = I(trans)/I(trans) + I(refl) where I(trans) is the number of photons for the localization on the transmitted path camera image, and I(refl) the number of photons for the localization on the reflected path camera image.
Crosstalk analysis Analysis of crosstalk from nanorulers images from each acquisition, on each channel, >19 nanorulers were selected (39-46 total per channel for S2C-DNA-PAINT and 70 total per channel for SD-dSTORM) by hand to ensure selection of intact nanorulers. A ROI was drawn around the selected Nanorulers and the ROI applied on both channels. The included localizations were counted using a dedicated macro ("Split Locs by ROI'' macro available from the ChriSTORM GitHub repository at https://github. com/cleterrier/ChriSTORM/) in Fiji software. 60  The number of localizations found in the ROI in the non-target channel (i. e. , localizations found in the longer wavelength channel, in a ROI drawn around a nanoruler in the shorter wavelength channel), were divided by the total of localizations found in the same ROI on both channels to calculate the crosstalk.
A python script was created to calculate mean and SEM (Scipy)  63  and render plots (Matplotlib). 64  Analysis of multicolor DNA-PAINT crosstalk from single-target samples DNA-PAINT crosstalk was evaluated using a two-step strategy with varying imagers (Figure  S3 ). A sample labeled for a single target and docking strand (example: microtubules with an F2-coupled secondary antibody) was first imaged with two non-target imagers (A1Ch1_NT: IF1-Atto565 and A1Ch2_NT: IF1-Atto643). After rinses, the same field of view was then imaged using a target and a non-target imager (A2Ch1_T: IF2-Atto565 and A2Ch2_NT: IF1-Atto643). Both acquisition sequences (A1 and A2) were processed using localization and optional demixing. Images for all channels from both acquisition sequences were reconstructed using the histogram method in the Fiji plugin ThunderSTORM,  61  where each pixel (15 nm in size) takes a value corresponding to the number of localizations inside this pixel.
ACKNOWLEDGMENTS We would like to acknowledge funding by the  Agence Nationale de la Recherche   ANR-20-CE16-021-03  to C. L. ; the Federation  pour la Recherche sur le Cerveau  ( AOE 16  '' Espoir en tête '' 2021) to C. L. ;  ANR-21-CE42-0015  to N. B. , S. L. F. , and C. L. ; and the  Association Nationale de la Recherche et de la Technologie (ANRT)  for the co-funding of K. F. and  A. M. ( CIFRE  PhD). This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement # 871124   Laserlab-Europe  ( JRA ALTIS ). We would like to thank  Marie-Jeanne Papandre  ´ou for discussions.
POEM procedure Procedural data collected included American Society of Anaesthesiologists (ASA) Physical Status Classification System score of the patient  [21] , date of procedure, full procedure (yes), length of myotomy, number of clips used to close the tunnel and the duration of the procedure. All POEM procedures were performed by a single operator (EC) according to the original description of the technique  [12]  and were adapted as detailed below using an anterior approach. A high-definition gastroscope (EG760Z or EG590, Fujifilm, Japan) mounted with an endoscopic submucosal dissection (ESD) cap on the distal tip was used for all procedures. POEM procedures were conducted under general anaesthesia with endotracheal intubation and CO2 insufflation. Anticoagulant therapy and antiplatelet agents (other than aspirin) were discontinued before the procedure.