dataesr/scientific-paragraphs-categorization huggingface dataset:
FUNDING We acknowledge funding from the  ARD2020 Biopharmaceutical program  of the  Région Centre Val de Loire (BioPROPHARM, CatharSIS , and ETOPOCentre projects),  EU   Horizon 2020 research and innovation program  (MIAMi projectgrant agreement N •  814645 ), and  ANR MIACYC  ( ANR-20-CE43-0010 ).
Acknowledgements Financial support from the  Agence Nationale de la Recherche, France , under grant agreement No. ANR-17-CE24-0025  (TOPSKY), the  DARPA   TEE program , through grant MIPR No. HR0011831554  and the  Horizon2020 Framework Programme  of the  European Commission , under  FET-Proactive Grant  agreement No. 824123  ( SKYTOP ), is acknowledged.
Molecular datasets from Rennes and Angers University A total of 125 primary non-G-CIMP GBMs were collected between 2004 and 2013 from the Neurosurgery Departments of Rennes and Angers University Hospitals (RAUH), including a new cohort of 77 samples (RAUH-new cohort  [6] ) pro led by In nium HumanMethylation450k BeadChip (Illumina Inc. ) and Agilent Whole HumanGenome 8 × 60K Microarray Kit (Agilent Technologies); and a published cohort of 48 samples (RAUH-GSE22891  [9] ) pro led by In nium HumanMethylation27k BeadChip (Illumina Inc. ) and Agilent Whole HumanGenome 4 × 44K Microarray Kit (Agilent Technologies). All patients were treated with surgery followed by RT plus concurrent and adjuvant TMZ. Sample collection, histological review, clinical follow-up and microarray pro ling were previously reported.
Molecular datasets from public databases Additional DNA methylation and gene expression microarray data were obtained from public databases, including the clinically annotated cohort from TCGA (TCGA-Brennan et al  [13] , RT/TMZ, n=219; RT alone, n=73), and three cohorts from the Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/): (1) GSE50923  [14] , RT/TMZ, n=49; (2) GSE60274  [15] , RT/TMZ, n=32; RT alone, n= 27; (3) GSE36278[16], unknown treatments, n=52. Moreover, DNA methylation microarray data of IDHwt LGGs were obtained from TCGA  [17]  (WHO grade II to III, various treatments, n=94), Chinese Glioma Genome Atlas (CGGA  [18] , WHO grade II to III, various treatments, n=30), GSE48462  [19]  (WHO III, RT/PCV, n=21; RT alone, n=34). For comparison, DNA methylation data of G-CIMP (or IDH mutant) tumors from TCGA  [13]  and CGGA  [18] , gene expression data of G-CIMP (or IDH mutant) tumors from TCGA  [13] , CGGA  [20]  and GSE16011  [21] , and molecular data of non-tumor brains (NTBs) from TCGA  [13] , CGGA  [18, 20] , GSE60274  [15] , GSE16011  [21]  and our RAUH datasets were all obtained.
In addition, DNA methylation data of 26 NTBs from GSE63347  [22]  were also downloaded for comparison of TCGA samples. Patient information for each included database was summarized in Table  S1. Probe selection and RISK score construction Initial CpGs probe selection was performed by removal of probes not covered on both 27k and 450k platforms, those targeting X and Y chromosomes, and those relevant to single-nucleotide polymorphisms (SNPs). A compendium of 842 genes has been recently identi ed as DDR genes in a CPRISPR-based study by Olivieri M et al  [4]. The 842 DDR genes were corresponded to the initially screened CpG probes based on gene symbols across PubMed gene database (https://www. ncbi. nlm. nih. gov/gene). A total of 1095 CpG probes corresponding to 635 DDR genes were kept for further selection.
To make DNA methylation microarray data comparable, batch effects between each platform and dataset were adjusted by M-value transformation and the empirical Bayes approach (ber R package)  [23]. Missing β values were imputed by impute R package. TCGA-Brennan et al and RAUH-new cohort were used for discovery phase. Selected CpGs with higher variability in methylation levels were used to correlate with OS by univariate Cox regression model and permutation test (Fig. 1 ). After the removal of inconsistent results from each discovery cohort, an overlap of 5 CpGs were kept and subjected to multivariate Cox model within meta-discovery cohorts adjusted by age, MGMT methylation status and treatment. Finally, the 5-CpG panel was identi ed for constructing a RISK score model, which was the sum of β values of each CpG weighted by their multivariate Cox coe cients, adjusted by age, MGMT methylation status, and treatment (Fig.
Gene set enrichment analysis Gene set enrichment analysis (GSEA) was run to evaluate functional pro les between grouped samples using the gene sets of the Gene Ontology Biological Processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) from the Molecular Signatures Database (MSigDB)  [25]. The proportion of 28 tumorin ltrating immune cells (TIICs) in tumor bulks was estimated using single-sample GSEA (ssGSEA) algorithm and the 782-gene signature reported by Charoentong et al  [26]  (http://software. broadinstitute. org/gsea/msigdb/index. jsp). The abundance of the 28 TIICs in tumor samples was summarized as normalized enrichment scores (NES). Tumor mutation burden (TMB) calculation Mutation annotation format (MAF) ies for 212 non-G-CIMP GBMs were retrieved from the TCGA database (https://portal.
Pyrosequencing Total DNA was extracted in using EZ DNA Methylation-Gold TM Kit (Beijing Tianmo). Pyrosequencing was performed by Pyromark Q96 ID platform and analyzed by PyroMark CpG software (Qiagen). The following primers were used: cg01251255-F: 5'-GTTTTGTAAGGYGTGTGTGTGAT-3'; cg01251255-R: biotin-5'-TCAAATTAACAAAAACTTAAAAACC-3'; and cg01251255-seq: 5'-TATTTGTAGGGGTAGGGAGT-3'.
Western blot Cell lysates were performed in RIPA buffer contained protease inhibitor and phosphatase inhibitor (Roche). The primary antibodies against IRF3 S396 (CST, #4947, 1:1000), IRF3 (CST, #4302S, 1:1000), P65 S536 (CST, #3033S, 1:1000), P65 (CST, #4764S, 1:1000), STING S366 (CST, #50907, 1:1000), STING (Abcam, ab171850, 1:1000), TBK1 S172 (CST, #5483, 1:1000), cGAS (Sigma Aldrich, #HPA031700, 1:1000), GAPDH (Proteintech, #60004-1-lg, 1:20000), NSUN5 (Abcam, #a121633, 1:1000) were used according to the manufacturers' recommendations. Each immunoblot was done at least thrice and the signals were quanti ed using ImageJ software (Bethesda).
2B ). The 5-CpG signature was also extended in cohorts of IDHwt LGGs (Fig. 2C ). Using the median risk score (0. 1617) as cutoff, patients from meta-cohorts (CGGA, TCGA and GSE48462 collectively) were divided into risk subgroups, where low-risk patients tended to have longer OS than high-risk ones (Fig. 2C ). Pooled analysis yielded signi cant OS difference between the risk subgroups (Fig. 2C ). Metaanalysis con rmed the marginally signi cant favorable OS in low-risk IDHwt LGGs (Fig. 2C ). Similar ndings were observed with the cutoff calculated by maxstat (Fig. S2 ). The 5-CpG signature was also validated in terms of PFS outcome in meta-cohorts of TCGA-Brennan et al, RAUH-new cohort and GSE36278 (Fig. S3 ). Risk classi cation of the 5-CpG signature in clinically or molecularly strati ed cohorts To further evaluate the prognostic performance, we applied the 5-CpG signature in strati ed cohorts by MGMT status (methylated vs.
4A ). The over-representation of mesenchymal subtype in high-risk tumors was also observed within non-TCGA samples (Fig. S6 ). GSEA on TCGA transcriptome data revealed very distinct expression pro les between the risk subgroups. As expected, DNA damage-related gene sets were enriched in high-risk tumors including intrinsic apoptotic signaling pathway in response to DNA damage and regulation of DNA damage response signal transduction by p53 class mediator (Fig. 4B  and Table  S2 ). More interestingly, high-risk tumors were found to be highly enriched with gene sets relevant with multiple regulatory processes of immune response, and in particular negative regulatory ones (Fig. 4B  and Table  S2 ). The enrichment of immune gene sets was mostly observed in high-risk tumors from non-TCGA tumors (Table  S2 ).
NSUN5 exhibited tumor-suppressor characteristics but conferred TMZ resistance to GBM cells To determine the effects of NSUN5 on malignant behaviors of U251 cells, a series of in vitro experimental assays were conducted. Knockdown of NSUN5 by small interfering RNA increased the proliferation rates of U251 cells (Fig. 6A ). Cell cycle analysis revealed that NSUN5 knockdown apparently increased the proportion of G2/M phase and slightly decreased the proportions of G0/G1 and S phases (Fig. 6B ). Flow cytometric analysis con rmed that depletion of NSUN5 inhibited apoptosis in U251 cells (Fig. 6H ). Moreover, NSUN5 knockdown enhanced the migration and invasion rates of U251 cells (Fig. 6C-D ). Within datasets of non-G-CIMP GBMs and IDHwt gliomas of grade II to IV, lower NSUN5 expressions were found to be associated with favorable OS (Fig.
6E-F ). These data indicated tumor-suppressor roles of NSUN5 in GBM cells. To investigate the effects of NSUN5 on TMZ sensitivity, the viabilities of U251 cells under the treatment of a series dose of TMZ were monitored. CCK-8 assays showed that NSUN5 knockdown signi cantly increased the TMZ concentration to reduce cell viability to 50% in U251 cells (Fig. 6G ). Depletion of NSUN5 also decreased the apoptotic rates of U251 cells when exposed to TMZ (Fig. 6H ). Western blot showed that NSUN5 knockdown was associated with decreased protein levels of p53 and p21 (Fig. 6I ). These data suggested that NSUN5 may confer TMZ resistance to GBM cells through mechanisms that involved p53-p21 pathway.
Acknowledgements We gratefully acknowledge all the patients who agreed to participate in this study and to those who have provided their medical care. We gratefully acknowledge  Dr. Menei ,  Dr. Le Reste ,  Dr. Vauleon ,  Dr. Quillien , and the  Tumor Banks from Angers and Rennes  for their constant support in the collection, processing and histological analysis of tumor samples. We also gratefully thank those who are willing to share valuable scienti c data ( Dr. Rose K Lai  and  Prof. Jill Barnholtz-Sloan ). I (AAY) want to thank my newborn daughter ( Jiayan Yin ) and my wife ( Dr. Yu Dong ) for their great support. The results published here are in part based upon data generated by  China Glioma Genome Atlas , The  Cancer Genome Atlas ,  German Cancer Research Center , the  Gene Expression Omnibus , and the research teams mentioned in this study.
Microscope Image Acquisition Images from fluorescently labeled HUVECs were acquired using a LSM 700 upright microscope equipped with a Plan-Apochromat 20×/0. 8 NA Ph2 objective. Images were taken at room temperature using Zen 2. 3 software. Bright-field images were taken using a Leica DMIL LED microscope equipped with a 10×/0. 22 NA Ph1 objective and a CCD camera (DFC3000 G). Images were acquired at room temperature while the cells were still in their culture medium using LAS X software (Leica). Images of aortas were taken using a LSM 780 inverted microscope (Zeiss) equipped with a Plan-Apochromat 20×/0. 8 NA Ph2 objective or with a Plan-Apochromat 63×/1. 4 NA DIC objective. The microscope was equipped with a photon multiplier tube detector. Images were taken at room temperature using Zen 2.
Cell Junction Activity Analysis Cell junction morphology analysis was done in HUVECs stained for VE-cadherin using the patch and classified Matlab code previously developed by  Bentley et al. (2014)  adapted for 2D images. Two status were defined: activated vs. stabilized, and divided into 3 level: from low to high. Activated junctions were defined as serrated or reticular and stabilized as straight thick junctions. Each image taken was divided into small pieces of images allowing to visualized only a portion of the junction and these small images were presented blindly and randomly to the user who then classified the junction accordingly. This technic ensured an unbiased analysis of the junctions regarding the treatment and regarding the global shape of the cell.
25 3 µm 3 , being the voxel isotropic) and afterwards, a selection of interesting cases were analyzed at the other facilities at higher spatial resolutions. 2. 4. 1. 3. 25 3 µm 3 Voxel Size XPCI-CT at ID17, ESRF These scans were realized in the imaging hutch of the ID17 beamline, placed around 150 m downstream the X-ray source. The sample was illuminated by a laminar, monochromatic 35 keV X-ray beam and placed 1. 8 m from the imaging detector, which is a sCMOS PCO. Edge 5. 5  [39] , equipped with a 50 µm-thick LuAG:Ce scintillator screen and a 2× magnification indirect conversion optics  [31]. For each CT scan, 4000 projections were acquired over 360° with an exposure time per projection of 30 ms. Every rat brain was entirely imaged requiring 5-6 vertical CT scans. The CT images were reconstructed using the filtered back-projection reconstruction method and the Paganin's phase retrieval algorithm  [40] , both implemented in the PyHST2 package  [41].
2. 4. 2. 1. 23 µ. m 3 Voxel Size XPCI-CT at P05, PETRA III These scans were obtained in the micro-tomography experimental hutch (EH2) with a 30 keV X-ray beam impinging on the sample placed at 50 cm from the CMOS KIT detector. For each CT scan, 6000 projections over 720° were acquired with an exposure time of 55 ms per projection. A 5× magnification optic system was used together with a 100 µm thick LuAG:Ce single crystal scintillator. The image reconstruction was performed with dedicated MATLAB  [42, 43]  scripts based on filtered back-projection and Paganin's algorithm. In some cases, maximum intensity projections (MIP) of a stack of N subsequent slices were computed. The MIP creates an output image containing in each pixel the maximum value over all images in the stack at that particular pixel location 2.
Tumor segmentation on MRI images The segmentation of tumor volumes was performed on T2w images acquired post Gd injection. Thus, the contrast agent uptake made the tumor well distinguishable from the rest of the brain tissue allowing an upper threshold-based segmentation procedure  [46]. Hemorrhage was excluded by the segmentation procedure since it produces a recognizable signal, i. e. , a T2w signal loss  [47]. For every imaged sample, a threshold in the grey scale values was set to segment the Gd-loaded tumor out from the surrounding tissues in MRI images. This was realized with the Analyze Particles plugin of the Fiji software  [48]  returning the number of pixels of the segmented area (tumor area) on each slice. The total volume of the tumor was obtained by multiplying the tumor area by the MRI slice-image thickness.
Tumor segmentation on XPCI-CT images After XPCI-CT image reconstruction, each sample dataset was prepared for tumor segmentation to be performed with the ilastik software  [49] , as shown in Figure  S1. The procedure is explained in the "tumor segmentation" section of the supplementary materials. The validation of the tumor segmentation is reported in Figure  S2 , where the segmented volume is validated by monoclonal mouse anti-glial fibrillary acidic protein (GFAP) staining on four different samples: results of this ilastik-based segmentation procedure show a good match for the GBM detection with the GFAP histology images. Nevertheless, areas with infiltrative tumor cells are overestimated by the segmentation procedure, as visible in Figure  S2d-d". The estimated tumor volumes are plotted against the animal survival; data of animals belonging to the same group, which died within 3 days, are averaged and reported as a single point.
3D image Rendering and Computing Aspects 3D rendering of the tumor and microcalcifications image volume were realized with the software VG Studio Max 3. 5 (Volume Graphics GmbH, Heidelberg, Germany) importing the full brain volume and separately the segmented tumor or microcalcification volumes. The image processing and segmentation procedures were carried out with a Fujitsu laptop with 4 Intel Core i7 CPU processors and 2. 5 GHz, a Fujitsu workstation with 8 Intel Xeon CPU processors with 4 kernels and 2. 6 GHz or via the ESRF Networked Interactive Computing Environment (NICE) where larger computational power was required. The segmentation procedures of a tumor volume on MRI images, on XPCI-CTs and microcalcifications volumes can take up to about 15 min, 5 h and 90 h, respectively. Usually, when performing microcalcifications segmentation the full dataset is divided in sub volumes that are segmented separately.
The sample-to-detector distance was set to 7098 and 243. 7 mm for SAXS in combination with XRF and WAXS, respectively, with exposure times of 0. 4 s for SAXS and XRF and 0. 3 s for WAXS. Data collection was performed in continuous vertical lines with the sample moving at constant speed while the detector was recording data frames with in-line rates of 1/0. 405 Hz for SAXS and XRF and 1/0. 305 Hz for WAXS. For SAXS data collection, a 7 m long air-evacuated flight tube was inserted between the sample and the detector. SAXS and WAXS 2D data were calibrated by silver behenate (SAXS) and NIST SRM640b (WAXS) and folded into 1D profiles. Details of the SAXS/WAXS and XRF data analysis are reported in the "SAXS/WAXS and XRF data analysis" section of the supplementary materials.
Radio-Induced Effects on Healthy Treated Rat Brains XPCI-CT coronal images of healthy BB, MB and MRT irradiated brains acquired with a voxel size of 3. 25 3 µm 3 are presented in Figure  2  and Figure  3  and correlated to histology results. All the images are displayed in the radiologic view and are obtained from samples harvested at D138, which was the decided sacrifice point for all the healthy animals. The main effects on the irradiated tissues caused by the three different treatment modalities are visualized with high contrast and detail. XPCI-CT data are then compared with H&E, Ca, Fe and GFAP stained histological images. XPCI-CT and histologic images of the BB-treated rat brains are visible in Figure  2a-c , where only the irradiated hemisphere is displayed. In all the irradiated brain regions only histology could reveal the presence of small structures in the BB10 and BB15-treated samples that were recognized as deposits of ferric ion with lateral dimensions of ~30 µm (see the insets of Figure  2b, c ).
Quantification and 3D Rendering of Radiotherapy Effects Results of the characterization and quantification study of tumor and microcalcifications for all the irradiation groups are reported in Figures  6  and 7. Tumor volumes graphs report both the volumes assessed with MRI and XPCI-CT according to the results of the compatibility test performed as explained in the Materials and Methods section. The compatibility test between MRI and XPCI-CT tumor volume measurements is reported in Table  S1  showing that, overall, the MRI and XPCI-CT segmentation procedures are compatible on six out of the eight considered samples. Furthermore, if the averaged values (last raw) of all the computed parameters are considered, MRI-and XPCI-CT-based tumor volumes are overall within the compatibility. As a result, the values of tumor volumes obtained from in-vivo MRI and ex vivo XPCI-CT data can be displayed in the same plot.
Although in this study the segmentation was performed on the full-organ 3. 25 3 µm 3 voxel size CT datasets, the multiscale approach applied in targeted regions allows for a better identification of small deposits, as shown in Figure  5. Microcalcifications are a well-known post-irradiation effect. Ca deposits are classified as a late effect of cranial irradiation in childhood  [58]. This effect is known as mineralizing microangiopathy, an autoimmune reaction localized to the irradiated area that is associated with vasculitis and hyalinization  [59]. Thus, hypersensitivity of the vessels after irradiation produces vascular damages with hypoxia that results in Ca mineralization. Mineralizing microangiopathy found after child and adult cranial irradiations is mainly present in the basal ganglia and is asymptomatic in most cases  [60].
Supplementary Materials: The following are available online at www. mdpi. com/article/10. 3390/cancers13194953/s1, [85] [86] [87] [88] [89] [90] [91] [92]. Figure  S1 : Ilastik segmentation workflow. The segmentation procedure is applied on a set of XPCI-CT images undergoing the procedure here displayed for one single image. Original 16bit images (1) are used to segment the tumor out and are subsequently reshaped for reducing the dataset size  (2). A variance filter is applied with a radius of 4 pixels (3): a homogeneous image without edges would be displayed in black, while in the presence of edges a brighter signal is displayed. For enhancing the tumor borders, the original image is divided by the filtered one (4). Thanks to the carving tool of the ilastik software, the object of interest and the back-ground can be labelled by the user  (5).
Based on that, the software identifies the object to be segmented. For a full dataset, it is sufficient to label the 10% of the available slices, the full-volume segmentation will be done by interpolation. After the tumor is correctly identified  (6) , binary images are produced  (7)  and the tumor volume can be quantified by the Analyze Particles Fiji plugin  (8). This volume can also be rendered in 3D via the software VGStudio MAX 4. 3 together with the original brain dataset  (9). Figure  S2 : Ilastik segmentation validation. For four samples the comparison of GFAP histology staining (first column) with the XPCI-CT images (second column) and the segment tumor (third column) is given. One slice for each sample is reported. Good agreement is shown for three samples (a-a"), (b-b") and (c-c"), while for the images (d-d") a good matching is visible for the solid tumor mass, but the ilastik segmentation overestimates the GBM infiltrations shown in the magnified insets.
The brain XPCI-CT dataset is given as semi-transparent volume while the macrocalcifications (mcs) are reported as colored solid deposits of dimensions according to the legend. This 3D rendering showcases that MRT200-induced microcalcifications are mainly present in the thalamus. S1. Compatibility study of the MRI-and XPCI-CT-based tumour volumes (TV) for the MB350 group. The table reports the animal name, the survival counted from the day of glioblastoma implant, XPCI-CT and MRI tumour volumes normalized to the full brain volume with errors and the compatibility evaluation of MRI-XPCI-CT values together with the statistical indicators P(t) and the confidence level (C. L. ). MRI-XPCI-CT tumour volumes with C. L. >5% are considered compatible each other. The last raw reports the average of the columns.
Acknowledgments: This study was supported by the  German Research Foundation (DFG)  within the  Research Training Group GRK 2274. Authors wish to thank the  ESRF  for the provision of the beamtime for the animal irradiation and sample imaging ( MD-1037  and  MD-1121 ) and the  ESRF-BMF  for animals housing and care and in particular  Alexandra Demory ,  Charlène Caloud ,  Loic De Saint Jean. We acknowledge the  Paul Scherrer Institut, Villigen, Switzerland  for provision of synchrotron radiation beamtime at beamlines X02DA-TOMCAT and X12SA-cSAXS of the  SLS  and would like to thank  Goran Lovric ,  Ana Diaz , and  Andreas Menzel  for assistance. D. K. acknowledges funding from the  SNSF  under Grant No. 200021_175905. We acknowledge  DESY (Hamburg, Germany) , a member of the  Helmholtz Association HGF , for the provision of experimental facilities.
LC-HRMS/MS analysis Liquid chromatography-mass spectrometry (LC-MS) analyses were carried out using Orbitrap Q Exactive mass spectrometer coupled to an UltiMate 3000 pump (Thermo Scientific, San Jose, CA). A heated electrospray ionization source (HESI-II) was used for the ionization of the target compounds. Data acquisition, calibration, and instrument control were performed using Xcalibur 2. 1 (Thermo Scientific) software. Samples were maintained at 15 °C in the autosampler and quality controls were injected before each analysis. A non-targeted screening LC-HRMS/MS method used for MN building was performed as follows : the mobile phases were composed of ammonium formiate at 2 mM and formic acid 0. 1% in water (phase A) and ammonium formiate at 2 mM and formic acid 0. 1% in methanol and acetonitrile (50/50) (phase B).