dataesr/scientific-paragraphs-categorization huggingface dataset:
Metabolomics: extraction, data acquisition, processing and filtering Extraction and LC-MS analyses: Briefly, specialized metabolites were extracted using 70% methanol. For all methanolic extracts, the separation of extracts was achieved using reverse phase liquid chromatography with a C18 column over a 10mn gradient (see Supplementary Material and Methods S3). We performed mass-spectrometry analyses with different instruments and settings to generate two datasets. The first dataset will be dedicated to genome-wide association analyses and the second one for annotations of metabolites. The first dataset, hereafter the "GWAS set", included all samples. For this analysis the chromatography flow was directed to an electrospray ionisation probe set to positive mode (-500 V endplate offset, +3. 5 kV capillary voltage, 2.
4 bar nebulizer, dry gas flow of 8. 0 L/min at 190˚ C) into a hybrid quadrupole time-of-flight (QTOF) mass spectrometer (Bruker, Bremen, Germany) with no collision energy. The mass-to-charge ratio ion scan was from m/z 50 to m/z 1500 with an acquisition frequency of 2 Hz. The mass spectrometer was m/zcalibrated with a 10 mM lithium formiate solution injected at the begin and the end of each chromatogram. The second dataset, hereafter the "annotation dataset", was generated from 56 randomly selected samples in the nine populations. We used the data dependent mode of the LTQ-Orbitrap Elite (ThermoScientific, Bremen, Germany), to generate MS² spectra for all sufficiently intense fragments. We ran this analysis on the plates of methanolic extracts twice, with the HESI electrospray ionisation probe operating in the positive and negative modes.
Parameter details are provided in Supplementary Material and Methods S4. Data treatment and filtering: For the "GWAS dataset", we converted raw proprietary files from the instrument to the mzML format using ProteoWizzard program  (Chambers et al. , 2012). We removed files with no calibration peaks and filtered remaining files to keep only data between 80 and 570 s using msconvert  (Chambers et al. , 2012)  to remove calibration peaks and column wash offs at the beginning and the end of the runs. Past these initial processing steps, all analyses were carried out in a home-made bioinformatic pipeline using Snakemake workflow manager, Singularity containers and R v4. 1. 1 (R Core team 2021) see (gitlab link). We used the IPO R package v1. 18  (Libiseller et al. , 2015)  to optimise parameters for retention time (RT) correction and peak picking implemented in the R package XCMS v3.
14  (Smith et al. , 2006)  (Table  S2 ). We ran the "findChromPeaks" function to detect chromatographic peaks. Then, we ran the "adjustRtime" function to correct RT deviation and calculated adjusted retention times using a subset-based alignment based on the QC samples regularly injected. Finally, we ran the "PeakDensityParam" and "groupChromPeaks" function to match all the detected peaks between samples. We produced a table of peak intensities and identified pseudo-molecules, based on RT and intensity correlations among peak groups using the "groupFWHM" and "groupCorr" functions of the CAMERA R package v1. 48. 0 (Carsten  Kuhl et al. , 2021). After that step we filtered the dataset to remove sample outliers and peak outliers. Samples from the eighth plate were removed because the mass spectrometer failed to m/z-calibrate.
Dataset S1) Metabolite annotations: We used the "annotation dataset" to annotate pseudo-molecules using an integrated metabolomic workflow  (Fraisier-Vannier et al. , 2020)  (see Supplementary Material and Methods S5). We obtained annotations for the "annotation set" and created a correspondence table between the "annotation dataset" measured on the LTQ-ORBITRAP-and the "GWAS-dataset" measured using the QTOF-MS with a m/z tolerance of 0. 03 and an RT tolerance of 0. 6 min (Dataset S2). Then, we selected putative annotations for 21 metabolites associated with genetic variation in oaks described below and manually validated annotations using MS2 spectra.
Population genomics of sessile oak populations In this study we sampled 225 sessile oak trees originating from nine populations (Fig. 1a ) and sequenced 224 of these trees to a depth of 10X (on average 36. 5M paired-end reads per tree). After filtering, we were able to genotype trees for 1,408,029 SNPs with a minor allele frequency above 10%, a missing genotype rate of 10% and after excluding regions annotated as transposable elements on the Q. robur reference genome, the closest genome available. We obtained an average SNP density of 170 SNPs per window of 100 kb, ranging from 0 to 923 SNPs, and 90% of 100 kb windows included over 9 SNPs (Fig. S1 ). Three pairs of individuals displayed genetic similarity above 95% and were removed from the dataset, bringing the total number of individuals genotyped to 218.
Using these 1. 4 million markers we estimated that linkage disequilibrium (LD, r²) decreased to values below 0. 2 over 2. 9 kb in our collection of 9 populations spanning a large fraction of the species distribution range (Fig. S2 ). We then pruned the dataset down to 356,413 un-correlated SNPs (r²<0. 2) in the 218 individuals to investigate population differentiation and genetic structure. First, we investigated the genetic differentiation between pairs of provenances using the Hudson fixation index FST. We found that FST between pairs of populations was on average 0. 008, ranging from 0. 002 to 0. 014 (Fig. 1b ). We observed the highest differentiation (FST=0. 0146) between two populations from France: Vachères and Saint-Sauvant (Fig. 1b ). Second, we investigated patterns of population structure using a PCA.
Leaf specialized metabolites (LSMs) characterisation We measured leaf SMs within oak leaves for 225 trees at two heights using a high-throughput untargeted LC-MS approach. Our raw dataset contained 750,540 signal intensities. After filtering, we obtained intensity signals for 219 pseudo-molecules in leaves from low and high branches of 209 and 215 trees, respectively. Given our sampling and LC-MS protocols, the 219 pseudo-molecules analysed in this study corresponded to non-volatile, moderately polar specialized metabolites. Based on MS2 data, we performed automatic annotation of the pseudo-molecules present in our leaf samples. We obtained putative annotations for 89 pseudo-molecules (out of 219) of the "GWAS set" (all samples, see material and methods) in negative and positive mode. These putative annotations were grouped in ontology classes, and included mostly flavonoids, terpenes, quinic acids and derivatives, hydrolyzable tannins, in addition to other rarer classes (Dataset S2).
These significant associations clustered in 155 loci, or mQTLs hereafter, separated by at least 1 Mb (see Material and Methods). Note that we did not account for associated SNPs mapped to scaffolds not assigned to chromosomes. For downstream analyses, we applied a more stringent filter and focused on 35 mQTLs including highly significant associations (-log10(p-value) > 10) for at least one pseudomolecule. These 35 loci were distributed on all chromosomes of the oak genome except on chromosome 12, and had sizes ranging from 1 bp to 1. 47 Mb (Fig. 3 ). For each of the 35 mQTLs, we investigated candidate genes and selected the pseudo-molecule with the strongest association for structural annotation (N=21 pseudo-molecules Table  1 , Dataset S3). The SNPs identified as associated with these 21 pseudo-molecules typically explained a large fraction of phenotypic variance (Fig.
4a , Fig. S5 ). We investigated the genotype distribution within the nine populations of those SNPs. For the 21 pseudo-molecules, we observed that the heterozygous and homozygous genotypes were distributed within the nine provenances without differentiation among populations (Fig. 4a , Fig. S5 ). Comparing the differentiation among populations for the genetic markers associated with the 21 pseudo-molecules to genome-wide patterns of differentiation revealed that only four out of these 21 markers displayed values above the 80th percentile (FST > 0. 02) of the genome-wide FST distribution (Fig. S6 ). For five of the 21 pseudo-molecules, the heterozygous genotype was associated with higher signal peak intensities than the homozygous genotype (Fig. 4b , Fig. S5 ). In addition, the homozygous genotype was not present in our dataset for 16 SNPs associated with seven pseudo-molecules (Fig.
Molecule annotation and candidate genes for leaf specialized metabolites with high association to genetic markers For the 21 pseudo-molecules with high associations within mQTLs, we sought to confirm their chemical structure using spectral proof from MS n analyses. Putative structures were proposed for only 12 of these molecules by the automated annotation pipeline (see Material and Methods). Of these, we were able to validate the structures of three, modify the proposed structure of four molecules (Dataset S4), and reject the proposed structures for five molecules. The seven manually validated molecules represented six different chemical structures that belonged to five different ontology chemical classes: flavonoids, quinic and gallic acid derivatives, lignan glycosides and cinnamic acids (Dataset S4).
On the other hand, the extensive genetic variation for leaf SMs we observed within all populations could be maintained by natural selection. While we discussed different possible scenarios that could explain the maintenance of this genetic variation, understanding the selective pressures that shape this variation and the ecological role it plays in forests is essential in the context of global change. Our results lay the foundation for detailed analysis of the selective pressures and the ecological consequences of the genetic variation of leaf SMs in oak dominated forests. Dataset 1: Pseudo-molecules intensities, sampling and peak descriptions. Dataset 2: Formula and automatic structural annotation of pseudo-molecules based on MS2 acquisitions. Dataset 3: GWAs associations results in mQTL clusters.
Acknowledgments This work was funded by successive grants to C. P. and B. B. between ( 2016-2019 ) from the  Idex of the university of Bordeaux ,  Action Thématique Transversale :  METAB-OAK "Natural variation of secondary metabolite production in  forest trees: the case of European white oaks". B. B. has received the support of the  European Union  in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of an.
Data Availability Codes and pipelines used for this study is available at: 1. https://forgemia. inra. fr/domitille. coq-etchegaray/gw-oak-snp-pipeline. git 2. https://forgemia. inra. fr/domitille. coq-etchegaray/gwas-gwoak-metaboak-pipeline. git 3. https://forgemia. inra. fr/domitille. coq-etchegaray/metab-oak-pipeline. git Raw metabolomics data and SNPs matrix will be available upon publication in the Dataverse INRAE Biogeco: doi:10. 57745/3CSJZ9 Raw WGS sequencing used for this study will be available upon publication in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB60751. (https://www. ebi. ac. uk/ena/browser/view/PRJEB60751).
Drug Screening Organoids were extracted from Matrigel and dissociated into single cells using TrypLE (together with ROCKi) and mechanical dispersion. The cell suspension was resuspended in full media. Each well of the 3 mm EM plate was seeded with 1000 cells and the cells were allowed to develop for three days. Every three days, a dilution series of doxifluridine (S2045, Selleckchem) or vehicle (DMSO) was applied to the organoid cultures. To determine the pharmacological response of organoids, cell viability was measured 6 days after treatment using the CellTiter-Glo assay (Promega) and live/dead staining (L3224, Invitrogen), according to the manufacturer's instructions. GraphPad Prism was used for both data analysis and IC 50 value estimation (version 8. 0. 1).
Viability Analysis (Live/Dead Staining and Cell-Titer-Glow Assay) For LIVE/DEAD labeling, organoids were incubated at 37 °C for 40 min with 50 mM calcein-AM and 25 mg/ml ethidium homodimer-1 (EthD-1; Molecular Probes, USA) and then photographed using confocal microscopy (Olympus, Japan). ImageJ software (NIH, Bethesda, MD, USA) was used to quantify calcein-AM (green, for live cells) and EthD-1 (red, for dead cells) signals. In addition, the vitality of the organoids was quantified using the CellTiter-Glo assay (Promega). Before measuring luminescence with a microplate reader (Thermo Fisher, USA), the plates were agitated at room temperature for 30 min. A dose-response curve was generated using GraphPad Prism to determine IC 50 values.
Expression intensity calculation Images from LIVE/DEAD assay are selected through Image J software (NIH, Bethesda, MD, USA). From the Analyze menu select "set measurements", area integrated intensity and mean gray value are selected. After selecting regions with cells, background regions are also selected from region next to cells. The expression intensity of LIVE/DEAD can be calculated by this equation: Integrated density -(area of selected cell × mean fluorescence of background readings).
Organoid-Distribution Evaluation Single cells dissociated from organoids were embedded in Matrigel at a seeding density of 2000 cells per well. The cells were then grown in a humidified CO 2 incubator at 37 °C, with medium changes every 3-4 days. Fourteen days after plating, the distribution of the organoids in each well was examined. Microscopic (Aliened Genetics, Korea) images of the organoids from each well were acquired and the images were processed using Image J software (NIH, Bethesda, MD, USA)  [26].
Acknowledgements YHJ designed, performed, and analyzed the experiments and wrote the manuscript; KWP, MSK,  DHC ,  JCA ,  KHN , and JCA assisted in performing the experiments and generating data. SBL, BSM; provided study materials provided HJO assisted with producing the platform. JAK and SC designed the conceptual ideas and research, supervised the study, and wrote the manuscript. SC designed the conceptual ideas and research, supervised the study, wrote the manuscript, and provided financial support.
Funding This work was supported by the  Technology Innovation Program  (No. 20012378 ), funded by the  Ministry of Trade, Industry & Energy (MOTIE, Korea). This work was supported by the  Technology Innovation Program  (No. 20008413 ), funded by the  Korean government (MSIT). This work was supported by the  Agency for Defense Development, Republic of Korea  ( UE211118ZD ,  311KK5-912888601 ).
Artificial intelligence (AI), through its subfields machine learning (ML), and deep learning (DL), through their supra-human capability to process vast datasets and to be trained using them, both show great promise in supporting the medical journey of patients affected by cerebrovascular malformations. Studies so far have shown how artificial intelligence can be helpful in predicting the risk of acquiring such conditions, detecting them, predicting the risk of rupture, predicting post-rupture complications, selecting the therapeutic strategy, or predicting the therapeutic outcome, with a huge potential to improve the patient's outcome  [1]. Experimental studies with artificial intelligence systems have demonstrated their efficacy and safety in the diagnosis and treatment of cerebrovascular malformations  [2].
Materials and Methods We retrospectively enrolled 29 patients from a database of 170 interventions performed between 2017 and 2022 in which these imaging analysis methods were used. In these 29 patients, simple analysis of 2D angiographic images could not provide the anatomical details desired by the neurointerventional radiology team. Analysis of images obtained by superimposing two 3DRA volumes was performed in 6 patients with cerebral aneurysm and 6 patients with AVMs and DAVFs. Biplanar angiography was performed standardly with Philips Allura FD 20 and Azurion FD 20 monoplane angiograph.
3D Workstation A C-arm angiography unit (Azurion/Allura Xper FD20, Philips Healthcare) connected to a dedicated 3D workstation (XtraVision, Philips Healthcare) was used. The patient was placed in a supine position in the angiography chamber. The patient's head was fixed with the aid of a headset in the neutral position. The isocenter was set at the level of the target lesion (aneurysm, AVM nidus). This primary dataset was then automatically transmitted to the workstation; the volume reconstruction appeared on the Workstation 8 s after the acquisition was finalized. We overlaid the primary dataset (3DRA, VasoCT, XperCT) with the secondary dataset (VasoCT, XperCT, MRI, 3DRA). Either manual or automated methods can be used to merge the vascular and anatomical landmarks. Sometimes, a fully manually controlled overlay is particularly useful if the automach function fails to overlay volumes.
In order to perform a controlled manual merge of two volumes accurately, a few tips and tricks are needed to make the final result useful in the decision algorithm. At the moment, only the initial software version (3DRA) of the dedicated workstation allows the merging of two imaging volumes, while the software version entitled SmartCT does not yet feature this function. When the application that performs the overlay function is launched, we find a first interface that parallels the two volumes we have selected (Figure  1A ). At this step, we have to choose a high Hounsfield unit object landmark (bone, endosaccular device, stent, coils) that we use to synchronize the volumes in all 3 planes (axial, sagittal, and coronal). Once the target point is placed in the same incidence on both volumes and in all 3 planes, we can open the second interface.
was fixed with the aid of a headset in the neutral position. The isocenter was set at the level of the target lesion (aneurysm, AVM nidus). This primary dataset was then automatically transmitted to the workstation; the volume reconstruction appeared on the Workstation 8 s after the acquisition was finalized. We overlaid the primary dataset (3DRA, VasoCT, XperCT) with the secondary dataset (VasoCT, XperCT, MRI, 3DRA). Either manual or automated methods can be used to merge the vascular and anatomical landmarks. Sometimes, a fully manually controlled overlay is particularly useful if the automach function fails to overlay volumes. In order to perform a controlled manual merge of two volumes accurately, a few tips and tricks are needed to make the final result useful in the decision algorithm.
At the moment, only the initial software version (3DRA) of the dedicated workstation allows the merging of two imaging volumes, while the software version entitled SmartCT does not yet feature this function. When the application that performs the overlay function is launched, we find a first interface that parallels the two volumes we have selected (Figure  1A ). At this step, we have to choose a high Hounsfield unit object landmark (bone, endosaccular device, stent, coils) that we use to synchronize the volumes in all 3 planes (axial, sagittal, and coronal). Once the target point is placed in the same incidence on both volumes and in all 3 planes, we can open the second interface. In the second interface (Figure  1B ), the screen is also split into two parts, and we have the possibility to analyze the superposition of the two volumes in different colors (redprimary volume, and blue-overlay volume) and in two spatial planes at the same time.
The 3D-3D Registration Algorithm The 3D-3D registration algorithm applied in the Interventional Workstation (Philips, Best, The Netherlands) searches the optimal rigid transformation according to a similarity measure, meaning that it translates and rotates the 3D dataset in 6 degrees of freedom to find the best match, but it does not deform the dataset. It employs Mutual Information  [8]  as a similarity metric, and the Powell algorithm  [9]  as an optimizer to find the best match. Mutual Information, in this case, expresses the mutual dependence between the grey values in both datasets for corresponding voxel locations. For the best matching spatial transformation, the corresponding anatomical structures map onto each other, and, therefore, a larger dependency between their grey values is reached.
This metric is particularly useful for multi-modality registration, as it does not make any assumptions on which grey value in one dataset should match which grey value in the other. In order to increase the capture range (the maximal distance between the datasets when starting the registration algorithm that still leads to a successful result), a multiresolution scheme is applied  [10]. First, the Powell algorithm is run with the 3DRA data downsampled to 64 3 voxels, and the multi-modal data are downsampled to match the 3DRA voxel size in mm. Then the registration process is repeated with the 3DRA data downsampled to 128 3 voxels and a matching voxel size for the multi-modal data. For both datasets, 256 grey value bins are used  [11]. Experimental results using a realistic head phantom yielded an average registration error of 0.
515 mm and an average rotation error of 0. 241 •  [12]. The capture range was determined using clinical data. For 3DRA-CT registration, 88% of the CT datasets could be registered successfully when the registration process was started within 30 mm translation offset. A total of 67% managed to robustly register within 50 mm translation. A total of 88% of the CT datasets still could be registered correctly to the 3DRA dataset when the initial rotation offset is 20 • , and 74% when the rotation offset is 30 •. For 3DRA-MR registration, 80% of the MR datasets can be registered correctly with an initial translation offset of 15 mm, and 84% yield correct results when the registration is started within 20 • rotation. In order to accelerate the registration algorithm, the GPU is used, leading to computation times of less than 8 s.
Given these elements, the decision was made to preserve the right PICA by segmental closure of the vertebral artery between the stents placed on the right vertebrobasilar axis and the emergence of the artery (Figure  6C, D, I ). Moreover, the overlay of the 3DRA volume of the left vertebrobasilar axis with the VasoCT examination of the same axis, performed after telescopic placement of three stents (Figure  6F ,G), helped to appreciate the areas of overlay between the distal and middle stent and also between the middle and proximal stent. As during MRI follow-up, a dimensional increase was observed (Figure  6J ), and it was decided to place a new flow-diverter stent inside the first stent, with the purpose of covering the aneurysmal neck. The sagittal XperCT volume pre-operative displayed near the sagittal XperCT volume post-operative, overlayed, but in mirror (independent function of Overlay XtraVision software), as in Figure  6K , provides information regarding potential bleeding from the giant basilar aneurysm in the posterior fossa, and can assess the mass effect and its relation with the posterior fossa craniectomy in such complex cases.
Given these elements, the decision was made to preserve the right PICA by segmental closure of the vertebral artery between the stents placed on the right vertebrobasilar axis and the emergence of the artery (Figure  6C, D, I ). Moreover, the overlay of the 3DRA volume of the left vertebrobasilar axis with the VasoCT examination of the same axis, performed after telescopic placement of three stents (Figure  6F ,G), helped to appreciate the areas of overlay between the distal and middle stent and also between the middle and proximal stent. As during MRI follow-up, a dimensional increase was observed (Figure  6J ), and it was decided to place a new flowdiverter stent inside the first stent, with the purpose of covering the aneurysmal neck. The sagittal XperCT volume pre-operative displayed near the sagittal XperCT volume post-operative, overlayed, but in mirror (independent function of Overlay XtraVision software), as in Figure  6K , provides information regarding potential bleeding from the giant basilar aneurysm in the posterior fossa, and can assess the mass effect and its relation with the posterior fossa craniectomy in such complex cases.