dataesr/scientific-paragraphs-categorization huggingface dataset:
Similar results were also obtained in the extended dataset of bladder cancer (IMvigor210) and melanoma (PRJEB23709) (Figures  S3A-E ). In addition, we analyzed the expression of targeted therapyand chemotherapy-related genes between high-and low-MRPRS groups (Figure  2J ). Interestingly, VEGF and mTOR pathway-related genes were found to be highly expressed in the low-MRPRS group, and MRPRS was negatively correlated with the expression level of angiogenesis-related genes including PECAM, FLT1/4, VWF, and CDH5 (Figures  2J, K ). In the E-MTAB-3267 cohort of ccRCC patients treated with sunitinib, the MRPRS was significantly lower in the response group than in the non-response group, and the low-MRPRS group showed a markedly prolonged survival (Figures  2L, M ). Collectively, our data suggest that the patients with high MRPRS may benefit from immunotherapy and those with low MRPRS may benefit from targeted therapy.
05). The MCP counter, xCell, CIBERSORT, and ssGSEA algorithm were used to calculate an immune score and to estimate the abundance of various types of immune cells. We found significantly higher estimates of Tregs, CD8 + T cells, NK cells, and B cells in ccRCC with high MRPRS (Figure  3C  and Figures  S5A-C ; Table  S7 ). Moreover, the VCAM1 + cell cluster presented upregulation of HLA-A and HLA-B (Figure  1E ), and it was also the cell cluster that communicates most frequently with immune cells (Figure  3E ; Table  S8 ). We performed volcano plots based on the DEGs from the high-and low-MRPRS groups. The results of the volcano plots showed that 1,780 genes were significantly upregulated in the comparison of the high-and low-MRPRS groups (Table  S9 ). In the PPI network from the STRING database with the Cystoscope software, we constructed a co-expression network consisting of 45 nodes and 169 edges (Figure  S4C ).
This is consistent with our finding that the positive correlation between MRPRS and immunotherapy benefits the bulk tissue datasets. It is interesting to note that the spatial localization of this immunotherapy-related ccRCC cell cluster is worthy of further investigation. We further explored the detailed role of m 6 A modification in modifying immune characteristics in ccRCC. The results of the GO and KEGG pathway analyses revealed a significant enrichment of genes in immune-related pathways. GO enrichment analysis showed that these DEGs were enriched in the humoral immune response, immunoglobulin complex, and antigen binding. The results of the KEGG analysis indicated these enriched pathways such as neuroactive ligand-receptor interaction, cytokine-cytokine receptor interaction, and the calcium signaling pathway.
The regulatory relationship between the m6A modification and the gene mutation still needs to be studied. Consequently, we provided a new perspective on the immune characteristics and immunotherapy strategies of ccRCC. However, several limitations should be recognized. Although we analyzed immune cell characteristics in a scRNA-seq dataset, the tumorinfiltrating immune cells were obtained based on algorithms, and thus, further experimental validation in vitro/in vivo is needed. Our study was also limited by the lack of clinical datasets to verify the relationship between the MRPRS and patients receiving targeted treatment or ICB combined targeted treatment. The combination of an MRPRS-based panel with prospective clinical trials is worth carrying out in the future.
(L) The MRPRS between response and non-response groups in ccRCC with sunitinib (E-MTAB-3267). (M) Kaplan-Meier analysis between high-and low-MRPRS groups in ccRCC with sunitinib (E-MTAB-3267) (*p < 0. 05; **p < 0. 01; ***p < 0. 001).
DATA AVAILABILITY STATEMENT All data used in this work can be acquired from the Gene-Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/) under the accession numbers GSE53757, GSE40435 and GSE29609, The Cancer Genome Atlas (TCGA) data portal (https://portal. gdc. cancer. gov/), The European Molecular Biology Laboratory (https://www. embl. org/), UALCAN (http:// ualcan. path. uab. edu/), HumanProtein Atlas (HPA, https:// www. proteinatlas. org/) and https://www. ebi. ac. uk/arrayexpress/ experiments/E-MTAB-3267/. The authors would like to thank the above database for the data provided.
g. ,  Mignot et al. , 2014; Barbieux et al. , 2019). The BGC-Argo profiling floats used in this study measured, among a suite of physical and biogeochemical properties, the c p and b bp coefficients and were both programmed to sample the entire water column at a high temporal resolution (four vertical profiles every 24 h), in order to monitor the diel variations of the bio-optical properties. We applied, for the first time, a modified version of the method of  Claustre et al. (2008)  to the diel c p and b bp measurements acquired by the BGC-Argo floats to derive community production. Using this dataset, we (1) assess the relevance of the diel-cyclebased method for estimating biological production of organic carbon in the considered regions and discuss the applicability of the method to b bp , in addition to c p ; (2) investigate the regional and vertical variability of the production estimates with a focus on the SCM layer in relation to the biological and abiotic context; and (3) discuss the relative contribution of the SCM layer to the water column community production.
BGC-Argo multi-profiling floats and data processing We deployed BGC-Argo floats programmed for "multiprofile" sampling in each of these two regions (Fig. 1 ). (hereafter fIon, WMO: 6902828) was deployed as part of the PEACETIME project  (Guieu et al. , 2020a). We used the fIon float measurements acquired during the time period 28 May to 11 September 2017. Thus, although collected in different years, the datasets arise from similar seasonal contexts. The BGC-Argo floats used in this study are of the type "PROVOR CTS4" (nke Instrumentation, Inc. ). They were both equipped with the following sensors and derived data products: (1) a CTD (conductivity-temperature-depth) sensor for depth, temperature and salinity; (2) a "remA" combo sensor that couples a Satlantic OCR-504 (for downwelling irradiance at three wavelengths in addition to photosynthetic available radiation, PAR) and a WET Labs ECO Puck Triplet (for both chlorophyll a (excitation and emission wavelengths of 470 nm and 695 nm) and colored dissolved organic matter (CDOM; 370 nm/460 nm) fluorescence and particulate backscattering coefficient at 700 nm); and (3) a WET Labs C-Rover (for particulate beam attenuation coefficient at 660 nm, 25 cm pathlength).
Data were collected along water column profiles from 1000 m up to the surface with a vertical resolution of 10 m between 1000 and 250 m, 1 m between 250 and 10 m, and 0. 2 m between 10 m and the surface. First, the BGC-Argo raw counts were converted into geophysical units by applying factory calibration. Second, we applied corrections following the BGC-Argo QC (quality control) procedures  (Schmechtig et al. , 2015 (Schmechtig et al. , , 2016;; Organelli et al. , 2017). Factory-calibrated chlorophyll fluorescence requires additional corrections for determining the chlorophyll a concentration (Chl). Values collected during daylight hours were corrected for non-photochemical quenching following  Xing et al. (2012). A global analysis of factory-calibrated chlorophyll fluorescence measured with WET Labs ECO sensors relative to concurrent chlorophyll a concentrations, deter-mined by high-performance liquid chromatography (HPLC), yielded a global overestimate bias of 2  (Roesler et al.
, 2017) , with statistically significant regional biases varying between 0. 5 and 6. The Mediterranean Sea is known to show very small regional variations of the fluorescence-to-Chl ratio  (Taillandier et al. , 2018) , with a mean value close to 2 (1. 66 ± 0. 28 and 1. 72 ± 0. 23 for the western and eastern Mediterranean, respectively;  Roesler et al. , 2017). Hence the bias correction factor of 2 was applied to BGC-Argo fluorescence data from both the Ligurian and Ionian regions, consistent with the processing performed at the Coriolis Data Center. For the particulate backscattering coefficient (b bp ), we followed the BGC-Argo calibration and quality control procedure of  Schmechtig et al. (2016). The backscattering coefficient at 700 nm (m -1 ) is retrieved following Eq. (  1 ): b bp (700) = 2 π χ βb bp -Darkb bp × Scaleb bp -βsw , (1) where χ = 1.
g. ,  Mignot et al. , 2014; Barbieux et al. , 2019)  and extrapolating the estimated relative contribution of the SCM layer to the water column production in the Ionian (F SCM ∼ 16 %), the SCM layer would contribute ∼ 7 % of the community production of the water column on a global scale (i. e. , F SCM of 16 % multiplied by a global spatial occurrence of 45 %). In addition, using a global BGC-Argo database,  Cornec et al. (2021)  estimated that SCMs in oligotrophic subtropical gyres behave as an SBM 8 %-42 % of the year, depending on the season. Thus, assuming the Ligurian SCM oligotrophic summer system as a reference for an SBM, the contribution of the SCM layer to the global water column production could seasonally reach 19 % (i. e. , F SCM of 42 % multiplied by a global spatial occurrence of 45 %).
Acknowledgements. Phytoplankton pigment analyses were performed at the SAPIGH national HPLC analytical service at the  Institut de la Mer de Villefranche (IMEV). We acknowledge the captains and crew of the Téthys and Pourquoi Pas? research vessels during the BOUSSOLE and PEACETIME cruises, as well as  David Antoine , PI (principal investigator) of the  BOUSSOLE  project, and  Cécile Guieu  and  Karine Desboeufs , PIs of the  PEACE-TIME  project. We thank the  international Argo program  and  Coriolis  project, which contributed to making the data freely and publicly available. Marin Cornec  is also warmly thanked for useful discussion regarding biological production in SCM systems. We finally wish to thank the two anonymous reviewers and the co-editor-inchief for their useful comments and suggestions.
Financial support. This research has been supported by the  PEACETIME program  (https://doi. org/10. 17600/17000300 ), a joint initiative of the MERMEX and ChArMEx components supported by  CNRS-INSU ,  IFREMER ,  CEA  and  Météo-France  as part of the program MISTRALS coordinated by INSU; PEACETIME-OC, supported by the  French program  CNES-TOSCA; remOcean, funded by  ERC  (grant no. 246777 ); and NAOS, funded by  ANR Equipex  (grant no. J11R107-F ). Marie Barbieux  was funded by a PhD grant from  Sorbonne Université  ( Ecole Doctorale   129 ). Review statement. This paper was edited by  Chiara Santinelli  and reviewed by two anonymous referees.
Multi operator image analysis and localisation error estimation We used deepBlink 27 , a convolutional neural network-based spot detection algorithm, for spot detection. We employed custom models trained on a combination of the following datasets: smFISH and SunTag datasets provided by deepBlink and in-house manually curated live cell imaging images. The models can be found https://github. com/zhanyinx/SPT_analysis/tree/main/models. The parameters and models used for each cell line can be found in Suppl. Table  S4. To detect 3D spots, we applied deepBlink to all z-stacks separately followed by linkage of the spots across z-stacks using Trackpy 45 ; the precise 3D coordinates of the spots are then determined using 3D gaussian fitting using a voxel of size 6x6x4 pixels centered at the spot in the brightest z stack.
3D spots are fed into TrackMate for tracking using linear assignment problem (LAP) tracker. Each track is assigned to manually annotated cell masks (from max z-projection of frame 93) using a custom script (https://github. com/zhanyinx/SPT_analysis/blob/main/source/spot_detection_tracking/assign_cellids. py), which uses the majority rule. Motion correction is then performed using a roto-translation model. Briefly, for each pair of consecutive frames, matches spots (i. e. same spot across time) are determined by solving the linear assignment problem using scipy. optimize. linear_sum_assignment function with the euclidean distance between spots as measure of distance. The roto-translation model is calculated for each pair of consecutive frames using the matched spots. Tracks with less than 10 spots are filtered out for followed up analysis.
Dual color image analysis and radial localisation uncertainty Spot detection and tracking is done as for the multi operator image analysis. We correct for chromatic aberration by computing the roto-translation on beads taken the same exact day of sample imaging. Briefly, beads spots are detected using deepBlink and matched across channels by solving the linear assignment problem using scipy. optimize. linear_sum_assignment function with the euclidean distance between spots as measure of distance. We used the set of matched beads spots across channels to compute the rototranslation. We apply the roto-translation to the sample images to correct for chromatic aberration. To increase the ability to detect longer tracks, we used an in-house script to stitch multiple tracks belonging to the same cell (https://github.
The membrane was washed three times in PBS+0. 1%Tween-20 and imaged on the Odyssey infrared imaging system (Li-Cor Biosciences). estimated from the images and images were deconvolved until one of the following stopping criteria was reached: The maximum number of iterations were performed (for random integrations: 20 cycles, for tdTomato and eGFP; in dual-color set-up: 15 cycles for tdTomato signal, for GFP signal: 5 cycles) or a quality change criterion below 0. 001 was returned. Representative image series shown in the main figures were deconvolved as described above, adjusted to display the same brightness and contrast and interpolated using a bicubic interpolation. Movies were corrected for bleaching over time using an exponential fit. The 2D projection of intensity changes over time was created using the the Temporal Color Code in Fiji (https://github.
Acknowledgements We would like to thank all members of the  Giorgetti lab  for help with labeling images for training the deepBlink spot-detection algorithm and feedback on the manuscript,  Anders S. Hansen ,  Leonid Mirny  and members of their labs for useful discussions of the results,  Elzo de Wit  for sharing the WAPL-AID-eGFP cells, and  Hubertus Kohler  for assistance with flow cytometry experiments. P. M. was supported by a  Marie Sklodowska-Curie Innovative Training Network  ( 813327  ' ChromDesign '). J. T. was supported by a  Marie Sklodowska-Curie Innovative Training Network  ( 813282  ' PEP-NET '). J. Z. was supported by a  Marie Sklodowska-Curie grant  ( 748091 -3DQuant ). Research in the  Giorgetti lab  is funded by the  Novartis Foundation , the  European Research Council (ERC)  ( 759366 , ' BioMeTre '),  Marie Sklodowska - Curie Innovative Training Networks (813327 'ChromDesign'  and  813282  ' PEP-NET ') under the European Union's  Horizon 2020 research and innovation program , and the  Swiss National Science Foundation  ( 310030_192642 ).
Significantly recurrent SVs In order to characterize SVs in pHGG including samples whose genomes had not been altered by treatment, we assembled a cohort of pHGG WGS data from 179 children including 61 hemispheric tumors and 118 diffuse midline gliomas, or DMGs. Of these, 61 are novel to this study (Supp. Table  1 ). Among the DMGs, 84 (71%) were from pretreatment biopsies including 33 obtained from the first multi-institutional North American clinical trial to incorporate diagnostic biopsies  35. The tumor purity of the pre-treatment biopsies was comparable to autopsy samples (median: 0. 8 vs. 0. 78, p = 0. 5) (Ext. Data Fig. 1A ). This represents the largest cohort of pretreatment DMGs with whole genome sequencing to date. In analyses of somatic copy-number alterations (SCNAs), we were able to detect a new recurrent amplification in 8q24.
We analyzed previously published H3K27ac enhancer tracks generated from H3 K27M mutant and wild-type pHGGs  42  and adult glioblastomas  43  and observed H3K27ac enhancer peaks within the MCR of the CCDC26 amplicon (Figure  1C , Ext. Data Fig. 2A ). We also validated the presence of the CCDC26 associated enhancer in an independent dataset that leveraged ATAC-seq to profile the chromatin landscape of an independent cohort of pHGGs  44. This enhancer also appears to be present in normal neural tissue available through ENCODE  45, 46. In contrast, enhancer maps from cells derived from hematopoietic and lung tissues did not show peaks at this location (Ext. Data Fig. 2A ). We conclude that the CCDC26 amplicon centers on a neural lineage-specific enhancer peak. Although this enhancer is present across neural tissues, histone mutant DMGs exhibit 31% more H3K27ac binding at this locus than H3 WT gliomas, constituting the fourth most differential super enhancer between these groups (q-value = 0.
05; p=0. 001)  42. Intriguingly, the CCDC26 amplicon within our dataset also occurred almost exclusively in H3 K27M gliomas (present in 14/97 H3 K27M vs. 1/82 H3 G34R mutant or H3 WT tumors; q = 0. 0018). These data indicate that the SRB in CCDC26 amplifies an enhancer which is present in the whole neural lineage but shows a quantitative enrichment specifically in H3 K27M gliomas. This lineage-specific enhancer also appears to interact directly with MYC. We interrogated Hi-C data from an H3. 3 K27M mutant pHGG, two patient-derived H3. 3 K27M mutant cell lines, and iPSC-derived neural progenitors, and found a prominent interaction peak between the MCR of the CCDC26 amplicon and the MYC promoter in all of them (Fig. 1D ; Ext. Data Fig. 2B ). We also observed increased MYC expression in pHGGs with this amplicon relative to samples without any SCNAs in the MYC TAD (Fig.
Sample acquisition This study includes published  [4] [5] [6] [7]  data available under EGAS00001000575, EGAS00001001139, EGAS00001000572 and EGAS00001000192. Novel data was generated from samples obtained from the DIPG-BATs clinical trial, the Dana-Farber Tissue Bank or collaborating institutions, under protocols approved by the institutional review board of the Dana-Farber/Harvard Cancer Center with informed consent. DNA and RNA were extracted from single DMG cores, pHGG biopsies, and autopsy samples using Qiagen AllPrep DNA/RNA extraction kits. Previously published pHGG whole genome sequencing (WGS) data  [4] [5] [6] [7]  and, if present, paired RNA-seq were acquired from public repositories.
Whole-genome sequencing Library preparation for paired end whole genome sequencing (WGS) was performed as previously described  17. In brief, genomic DNA was fragmented and prepped for sequencing (to 60X depth for tumors and 30X depth for normal samples) on an Illumina HiSeq 2000 instrument. Reads from both novel and published data were aligned to the reference genome hg19/ GRCh37 with BWA  79  , duplicate-marked, and indexed using SAMtools and Picard. Base quality score was bias adjusted for flowcell, lane, dinucleotide context, and machine cycle and recalibrated, and local realignment around insertions or deletions (indels) was achieved using the Genome Analysis Toolkit. All paired samples underwent quality control testing to ensure accuracy of tumor-normal pairs.
Structural variant detection and significance analysis SvABA  36  was used to call SV in paired tumor normal mode with default parameters. In addition to filtering germline variants against the paired normal, telomers and centromeres were blacklisted. The significance of recurrence analysis was performed separately for breakpoints (1D) and juxtapositions (2D) analogous to the previously published approach for pan-cancer  22  and adapted to the pHGG context. For the significance analysis of recurrent breakpoints (1D ) as previously described  22  , the genome was binned into 50kbp bins with 500bp overlap. The bins were annotated by naming them after the TAD they were located in, with the name of the TAD derived from the Cosmic Cancer Gene Census 53 gene in the TAD. Germinal zone TAD boundaries from GSE77565  86  were used as the closest available normal neural progenitor.
Eligible territory  87  was defined by masking low-complexity genomic loci based on https://github. com/lh3/sgdpfermi/releases/download/v1/um35-hs37d5. bed. gz. Only one SV per sample per bin was counted. Fishhook  88  was used to calculate a background model for the likelihood of a breakpoint in each bin, based on six covariates: replication timing (from http://mskilab. com/fishHook/hg19/RT_NHEK_Keratinocytes_Int92817591_hg19. rds), GC content (from http://hgdownload. cse. ucsc. edu/goldenPath/hg19/gc5Base/hg19. gc5Base. txt. gz), presence of SINE elements (http://www. repeatmasker. org), fragility (from https://data. broadinstitute. org/pcawg6sv/1D_covariates/fragile_genes_smith. hg19fp. txt (adapted from  89  , mappability (from http://hgdownload. cse. ucsc. edu/goldenPath/hg19/encodeDCC/wgEncodeMapability/), and heterochromatin status.
ChromHMM for the caudate nucleus was chosen for the heterochromatin track based epigenetic similarity to pHGG (Ext. Data Fig. 2A ). These data were downloaded from https://egg2. wustl. edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/coreMarks/jointM odel/final/ E068_15_coreMarks_mnemonics. bed. gz and subset to ['8_ZNF/Rpts', '9_Het', '15_Quies', '14_ReprPCWk'] to define heterochromatin. All these covariates have been repeatedly shown to be predictive of the probability of a SV occurring in the bin  22, 23, 88, 90  and p-values for the influence of each individual covariate on the model ranged from 2x10 -16 to 0. 21. p-values reflecting the significance of breakpoint enrichment were then calculated and corrected for multiple hypothesis testing using the Benjamini-Hochberg procedure  91.
SV signature analysis SV signature analysis followed previously published approaches  23, 26  , with slight modifications for overall lower SV counts in pediatric tumors. In brief, SVs were stratified according to the span between the two breakpoints (0-30kbp, 0. 03-1Mbp, >1Mbp, interchromosomal); read orientation (deletion, duplication, inversion and interchromosomal); and whether they were clustered or simple based on the comparison between local SV density and the overall SV density in the sample, as determined by clusterSV  23. This resulted in a count matrix of 20 SV feature combinations x 179 pHGGs, which was analyzed with Bayesian NMF using SignatureAnalyzer  24, 25  code to determine the optimal, most informative number of signatures and signature activities in each sample.
ChIP-seq analysis Previously published H3K27ac ChIP-seq sequencing data from primary DMGs were downloaded from GSE128745  42. Peaks were called using MACS2  98  callpeak with -B -SPMR to save the fragment pileup per million reads track. The resulting bdg-files were used to calculate fold enrichment and q-value tracks with macs2 bdgcmp which were transformed into bigwig files with rtracklayer and visualized together with copy number and SV calls in gtrack. Additional bigwig files for adult GBM H3K27ac, pHGG ATAC seq and non-cancerous or non-brain tissues were downloaded from GSE54792  43  , GSE126319  44  and the Encode project  45, 46  respectively.
Data processing Additional Hi-C files for NPCs (neural progenitor cells) were downloaded from www. synapse. org/#!Synapse:syn12979101 (registration required; Data Download -Study "iPSC-HiC")  100. Analysis of Hi-C generated and downloaded fastq files was performed using Juicer and the associated Juicer Tools  101. Contact maps were generated using Juicer with the following parameters: -s DpnII -g hg19. Map resolution was determined by using Juicer's "calculate_map_resolution. sh" script. Hi-C contact maps and associated annotations were visualized using Juicebox  101. The HIFI algorithm  102  was used to process 5-kb resolution Hi-C data to obtain higher accuracy estimates of interaction frequencies, using the following parameters: bandSize=1000, outputNormalized, boundaryKS=1000.