dataesr/scientific-paragraphs-categorization huggingface dataset:
2a ). Hypotheses are envisioned as a network where variables are nodes, and paths connecting them represent causal relationships. In SEM, the effects of multiple predictors are simultaneously assessed for multiple response variables  (Shipley 2016). We implemented structural equation models using the piecewiseSEM package (version 2. 0. 2), which uses a local estimation approach for models in the hypothesis network allowing for the incorporation of more complex model types  (Lefcheck et al. 2019 ). Model fit is evaluated using tests of directed separation  (Shipley 2016) , which determine whether an association exists between two variables in the network conditional on each of their causes. If two variables are not conditionally independent, the model is updated by adding a path between them to make the model more consistent with the data.
With this random effect structure, we thus allow relationships to vary among species and do not assume effects will be of the same magnitude or direction for all species. We fit the hierarchical model for genetic diversity using the lme4 package  (Bates et al. 2015)  within piecewiseSEM. We scaled and centered all variables before analysis so path coefficients were comparable. Finally, we checked model residuals for spatial autocorrelation using Moran tests. We conducted SEM analyses in parallel for each of our 4 buffer sizes.
Acknowledgements: We would like to thank  Mitchell Green  for assistance with data acquisition, as well as the authors of the original datasets for making their data public. C. S. and C. J. G. were supported by a  Natural Sciences and Engineering Research Council of Canada   Discovery Grant  to C. J. G. C. S. was also supported by a  U. Manitoba Graduate Fellowship , and a  U. Manitoba Graduate Enhancement of Tri-council funding  grant to C. J. G.
The vascular endothelium is a hot spot in the response to radiation therapy for both tumors and normal tissues. To improve patient outcomes, interpretable systemic hypotheses are needed to help radiobiologists and radiation oncologists propose endothelial targets that could protect normal tissues from the adverse effects of radiation therapy and/or enhance its antitumor potential. To this end, we captured the kinetics of multi-omics layers-i. e. miRNome, targeted transcriptome, proteome, and metabolome-in irradiated primary human endothelial cells cultured in vitro. We then designed a strategy of deep learning as in convolutional graph networks that facilitates unsupervised high-level feature extraction of important omics data to learn how ionizing radiation-induced endothelial dysfunction may evolve over time.
In this work, we propose a systemic analysis of ionizing radiation-induced dysfunction in vascular endothelial cells using topological data analysis  (Zomorodian and Carlsson, 2004)  and deep learning on graphs  (Hamilton et al. , 2017a, b). Both of these approaches are enhanced by systems biology methods  (Heinonen et al. , 2015 (Heinonen et al. , , 2018;; Herskind et al. , 2016)  that significantly improve the characterization of biological processes across the heterogeneous samples of omics layers assembled for this study. These datasets describe the proteome, targeted transcriptome, metabolome, and miRNome of primary human endothelial cells grown in vitro, and the results are aligned with experimental validation steps. Briefly, this interdisciplinary approach was carried out in three main steps.
First, we defined two feature representations of the data, i. e. early and late cell dysfunction based on the proximity of the sample to the response of vascular endothelial cells to irradiation over time at an irradiation dose of 20 Gy. This distinc tion allowed us to identify the molecular signatures of vascular dysfunction on each dataset. To do this, we used persistent homology algorithms based on discrete Morse theory  (Scoville, 2019). We then integrated each signature into a global gene regulatory network (OGRN)  (Chen and Mar, 2018) , which led to identification of the global molecular signature. Last, this network was dynamically modeled using semisupervised learning of the two feature representations  (Ding et al. , 2018) , providing the top five candi dates per feature for experimental validation.
Dataset time profiles We collected 4 omics datasets from unexposed HUVECS and HUVECS exposed to 20 Gy of ionizing radi ation (12 h-21 days after irradiation, 8 time points) (Figure  1 ). For the miRNomics and the transcriptomics studies, wefocused on microRNAs (miRNAs) and genes of interest usingTaqMan array microfluidic cards to assess levels of expression using RT-qPCR. We thus generated expression matrices containing the 2~ACt values of 293 miRNAs (Data S1) and 308 gene transcripts (Data S2) over time. For the proteomics study, In particular, the proteomics mapper graph (Video S3) is determined by 7 p-cubes. This relatively high numberof cubes mainly traces the full path going from early to late endothelial cell dysfunction following a dose of 20 Gy. In contrast, the other omics datasets showed a much lower number of p-cubes: the transcriptome by 3 p-cubes (Video S4), the miRNome, and the metabolome by 2 p-cubes (respectively Videos S2 and S5).
Global molecular signature of the irradiated endothelium Molecules in each omics dataset subject to functional reprogramming were identified by considering overlapping entities in the p-cubes assembly performed by the mapper algorithm. Subsequently, a thorough examination of the mapper graphs described in the previous section identified 83 candidate checkpoints controlling the response of endothelial cells to irradiation (Table  1 ). We inferred miRNA targets from the TarBase database, one of the embedded databases in DIANA webtools, leading to the identification of the CDKN2A gene as a target of hsa-miR-10b-5p, which is strongly linked to radiation-induced senescence in vitro and in vivo  (Lafargue et al. , 2017; Soysouvanh et al. , 2020).
, 2010; Guipaud et al. , 2018; Korpela and Liu, 2014). Interestingly, more than half of the top enriched cellular processes may be directly or indirectly related to angiogenesis, suggesting that a large portion of the identified entities are related to angiogenesis and that this process is strongly altered by ionizing radiation in our study. Taking this further, we used the subnetwork enrichment analysis module in Pathway Studio software to search for protein regulators and protein targets, and then we searched for enriched regulated processes and cells using the 83 candidates and 200 most enriched regulated proteins (Figure  3 ). The networkbuilt using Pathway Studio and Cytoscape (Figure  3B ) confirms the previous analysis of GO pathways/groups enriched with the 83   We built a hybrid dynamic model based on the semi-supervised learning of vascular endothelial cell dysfunction after irradiation (Figure  5 ).
Considering all these data and our own results, this in vitro model could recapitulate the steps that lead irradiated endothelial cells to become senescent in vivo. To decipher the molecular response of endothelial cells, we analyzed the miRNA, transcript, protein, and metabolite contents of primary vascular endothelial cells to model the continuum of radiation-induced injury processes between early and late vascular dysfunction in healthy tissue and tumors during radiation therapy. The great depth of omics datasets exploited is a major advantage of this analysis to fill the gap in our understanding between early and late endothelial dysfunction, which is thought to contribute signifi cantly to the tissue effects of radiation therapy. This allowed us to propose molecular targets related to the radiation-induced alteration of angiogenesis, a fundamental function of endothelial cells.
In this way, this accumulation of samples suggests a real effect due to the time period considered after irradiation. Our analysis accounts for such an effect by translating each dataset into time expression matrices subject to these models. A regression of the datasets to their fold-change shapes allowed for proper interpretation of the results in the subsequent parts of the study. At some stages, a projection of the datasets preserving their original shape was useful to create the topolog ical skeleton of each omics layer. In each of these discrete spaces, we were able to compare groups of elements in a homological way, converting these spaces into continuous omics spaces, conveniently represented by graphs (discrete Morse theory used in the mapper algorithm). This allowed us to identify topologically important molecular feature modules to perform continuous conversions between omics spaces, which goes beyond descriptive clustering.
In particular, the global structure of our complex data can be rigorously quantified through those topological invariants. As invariant, those representations became ideal features to be used as the basis of the graph neural network  (Tauzin et al. , 2020) , herein applied to dynamically learn molecular interactions and patterns  (Knyazev et al. , 2018; Rhee et al. , 2018)  occurring within cells after irradiation. and, ultimately, fibrosis predicted by the miRNome. These three sets of graphs accurately mapped the deregulated pathways required to release cellular stress along the endothelial barrier disruption as well as the alteration of the angiogenesis process. Their dynamics were obtained naturally by applying spectral convolutional aggregation on the nodes of the omics integrated network.
mRNA expression analysis Reverse transcription was performed usingtheHigh Capacity Reverse Transcription Kit (Thermo Fisher Sci entific) according to the manufacturer's instructions. Fortargeted transcriptomics studies, gene expression assays were performed using pre-made TaqMan low-density 384-well arrays (TLDA) for human angiogenesis, inflammation, apoptosis, immune response and protein kinase panels (Thermo Fisher Scientific). cDNA (400 ng per sample) was loaded onto the port of each gene signature array card and PCR was per formed using the ABI PRISM 7900 Sequence detection system (Thermo Fisher Scientific). Data Assist soft ware (Thermo Fisher Scientific) was used to determine 2-AACt values, with the following criteria: a maximum allowable Ct value at 37 was set and maximum Ct values were not included in calculations.
Normalization was performed using a global normalization method on a persample basis  (Mestdagh et al. , 2009). Forspecific gene expression studies, pre-developed TaqMan Gene Expression Assays (Thermo Fisher Scientific) were used and PCR was performed with the ABI PRISM 7900 Sequence detection system. For in vitro experiments, PCR fluorescent signals were normalized to a PCR fluorescent signal obtained from the housekeeping ACTB gene (for mRNAs) orthe U6 snRNA (for miRNAs). For in vivo experiments, normalization was performed using the global normalization method on a per sample basis as indicated above. For unsupervised hierarchical clustering analyses and heatmap creation using Data Assist software, distances between samples were calculated for hierarchical clustering based on the DCT values using Pearson's correlation, assay centric as map type, and average linkage as clustering method.
In each card, a panel of 4 housekeeping microRNAs was included (Thermo Fisher Scientific). These 312 miRNAs were measured in triplicate at 0. 5, 1, 2, 3, 4, 7, 14 and 21 days after 0 and 20 Gy. The global matrix was analyzed using Data Assist software (Thermo Fisher Scientific) and a final list of 289 miRNAs was used for implementation in the computational analysis including all biological entities.
OPEN ACCESS Liquid chromatography-electrospray ionization MS and data analysis of proteins. Peptides were resuspended in 15-80 mL of 3% (v/v) acetonitrile/0. 2% (v/v) formic acid depending on the initial volume ofthe fraction and then analyzed with a nano-LC1200 system coupled to a Q-TOF 6520 mass spectrometer equipped with a nanospray source and an HPLC-chip cube interface (Agilent Technologies). Briefly, peptides were enriched and desalted on a 160-nL RP-C18 trap column and separated on a Zorbax C18 column (30-nm pore size, 5-pm particle size, 150-mm long 75 mm inner diameter; Agilent Technologies) using a 55-min linear gradient (3-75% acetonitrile in 0. 1% formic acid) at a flow rate of 400 nL/min. Full autoMS1 scans from 290 to 2,400 m/z and autoMS2 from 59 to 3,200 m/z were recorded. In each cycle, up to five precursors sorted by charge state (preferred 2 + ions and single-charge ions excluded) were isolated and fragmented in the collision cell.
The collision cell energy was automatically adjusted according to the m/z ratio. Active exclusion of these precursors was enabled after 1 spectrum in 0. 2 min, and the precursor selection threshold was set at 1,000 counts. Peptide and protein identification were performed using the Spectrum and peptides were auto-validated using default parameters. Validated peptides were then exported in an Excel. ssv file using the following filter parameters: score of peptides >6 and % of SPI >60. Last, differential protein expression of iTRAQ labeling data was performed using iQuantitator open source software and the eDesign default parameter value as described  (Schwacke et al. , 2009).
Ultra-high-performance liquid chromatography-high resolution mass spectrometry analysis of metabolites. Extracted metabolites were analyzed using reverse phase liquid chromatography hyphenated to high-resolution mass spectrometry. Chromatographic separation was carried out on a Dionex UltiMate 3,000 (Thermo Fisher Scientific) operated by Chromeleon 6. 8 software. 5 mL of each sample was injected in a Hypersil Gold (100 mm x 2. 1 mm x 1. 9 mm) column (Thermo Fisher Scientific). The column was kept at 40°C and the flow rate was maintained at 400 mL/mL. Mobile phases were 0. 1% formic acid so lutions in water (A) and acetonitrile (B). For the first minute, a 0% of B was kept isocratically following by 10 min of linear gradient up to 100% B which was maintained in isocratic mode for 2 min. Initial conditions were reached in 1 minandthe column equilibrated for 2 min.
Instrument setup and LC-MS system control were achieved under Xcalibur 3. 0. 63 spectrometer software (Thermo Fisher Scientific) and control of the mass spectrometer was achieved using Tune Q Exactive Plus 2. 5 application (Thermo Fisher Scientific). The samples were analyzed in the following order: blank samples were analyzed 5 times, then diluted pools were analyzed in triplicate from most diluted to least diluted, followed by experimental samples in random order and spaced every 5 samples by a pool sample. At the end, a pool sample was subjected to a High Collision Dissociation (HCD) experiment using Data Dependent Analysis in orderto obtain structural infor mation from the MS/MS spectra. Pre-processing of metabolomics data. Raw data were converted into mzXML files using ProteoWizard  (Chambers et al.
, 2012). Data extraction was achieved using XCMS library underthe R environment forthe positive and negative ion modes separately. Peak detection was performed using the centWave method. Chromatographic peak width was set from 2 to 15 s, signal to noise threshold at 3, while the noise was set to 10,000 and the m/z tolerance between two consecutive scans was set to 5 ppm. Pre-filtering of detected peaks was set to 4 consecutive scans with intensities higher than 50,000. Peak alignment between samples and peak grouping were achieved using the Obiwarp algorithm and density method, respectively. Extracted data matrices were then filtered in order to eliminate analytical background and correct analytical drift. Peaks coming from the blank samples and from instrument noise were removed from the matrix, together with any peaks that varied by more than 30% in all pool samples.
Peaks in diluted samples that did not linearly increase in line with concentration were removed for all samples. Analytical batch drift was corrected using the van der Kloet algorithm (van der  Kloet et al. , 2009). The final matrices contained 661 ions in the positive ionization mode and 658 ions in the negative mode. The two-ionization matrices were used separately forthe untargeted metabolomics analysis. Forthe semi-targeted approach, matrices were matched to our in-house database, containing retention time and m/z information on about 1,300 metabolites for the peak annotation. Matching was performed using Workflow4Metabolomics  (Giacomoni et al. , 2015) , using the LC-MS annotation with bank in-house tab  (Rochat, 2017). The final matrix contains 115 metabolites identified with different levels of identification.
Omics data visualization and denoising Before any analysis, the data were projected into a three-dimensional space using the t-distributed stochastic neighbor embedding (t-SNE) method (van der  Maaten and Hinton, 2008). The aim of such transla tion was to explore the eventual presence of recurrent profiles, both in time and in expression. Since only the protein dataset was expressed in fold-change terms, and with a view to completing a homogeneous comparison, we linearly regressed to their fold-change-like ratio transcripts, metabolites and miRNAs. This may be calculated by solving the following optimization problem  (Morilla and Ranea, 2017) : y = X b + s, where typically y is the vector of observed values, i. e. up-or downregulated relative to their prior measurement, XB the product between the matrices X(dataset expression) and B (variable to be calculated), and S is the correlated random error which follows a normal distribution.
Construction of the irradiated complexes In this step of the analysis, we intended to contract the discrete space determined by the projection of the data denoised in the previous section to a continuous space. The rationale underlying this transformation was to identify lasting modules. To this end, we constructed simplicial complexes making use of the Vietoris-Rips filter  (Hausmann, 1995) , which provides a suitable method for associating vector spaces using linear maps. This topological configuration (i. e. the nerve theorem  (Borsuk, 1948) ) would enhance by homology  (Stillwell, 1993)  those clusters relevant to successfully completing the entire conversion. Those clus ters are referred to as persistence modules and within them, they contain precious information on the high dimensional structure of any natural dataset.
''reference'' miRNA; those are originated from the same hairpi n structure (pri-and pre-miRNA). When the data are not suffici ent to determine which sequence is the predominant one, the majority sense in DIANA database is considered and put inside brackets (see Supplemental Information text for further details on their gene targets). bSee Supplemental Information text for further details on their associated enzymes. (cell death and apoptosis). In addition, it highlights otherpreviousiy known pro cesses involved in the response of endothelial cells to irradiation, i. e. aging and senescence, as well as a connection to the immune response through cell adhesion and relationships with leukocytes, especially mac rophages. Last, it indicates angiogenesis as an important process in response to irradiation.
ACKNOWLEDGMENTS This work is supported by  Electricités de France (Groupe Gestion Projet Radioprotection) , the  Institut de Radioprotection et de Surete Nucleeaire  ( ROSIRIS program ),  INSERM , the  University of Normandy-Rouen , the  Region Normandie , and the  European Union. Europe gets involved in Normandy with the  European Regional Development Fund (ERDF). The funding agencies were not involved in any research plan relative to the study design, data collection and analysis, decision to publish, or drafting of the manuscript.
Data and code availability • The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  (Perez-Riverol et al. , 2019)  partner repository with accession number PRIDE: PXD030572. The mass spectrometry metabolomics data have been deposited at the MetaboLights database  (Haug et al. , 2020)  with accession number MetaboLights: MTBLS3680. Both datasets are publicly available as of the date of publication. Accession numbers are listed in the key resources table. Raw data from real-time quantitative PCR and microscopy data reported in this paper will be shared by the lead contact upon request. The miRNA, mRNA, proteome and metabolome processed data reported in this study are supplied as Data S1, S2, S3, and S4.
For example, although the enrichment factor was below the selected threshold at 6 and 10 DAS for ethylene pathway, 118 and 105 genes were found to be induced, respectively (Supplementary Table  S3 ). To obtain a more detailed view of the well-known defense pathways, we compared the number of their respective assigned up-regulated genes by A. brassicicola at each developmental stage. We extracted from the RNA-seq dataset all the assigned genes related to JA metabolic process (GO:0009694), indole-containing compound metabolic process (GO:0042430), glucosinolate (GSL) biosynthetic process (GO:0019761), phytoalexin biosynthetic process (GO:0052315), response to ROS (GO:0000302), response to ET (GO:0009723) and response to SA (GO:0009751). We identified 0 to 26 induced genes assigned to JA metabolism, 57 to 115 assigned to indole-containing compound, 0 to 62 assigned to response to ROS, 41 to 105 assigned to ET pathway and 92 to 269 assigned to SA response (Supplementary Figure  S1  and Supplementary Table  S3 ).
brassicicola in our RNA-seq dataset. In addition, some mutants altered in mucilage composition and structure such as cesa5  [51] , mum2  [52] , csla2  [53]  and gl2  [54]  were included. These latter mutant lines have impairments in genes that were shown to be Plants 2022,  11, 1708  repressed by A. brassicicola and have a structural and/or biochemical contribution to the interface contact between the seed tissue and the pathogen, making them good candidate genes for the entry of necrotrophic fungus into plant/seed tissues. Germination rates of healthy and A. brassicicola-infected seed lots were first determined at 3, 6 and 10 DAS in the mutants impaired in defense pathways (Figure  4 ). As previously described for the WT Col-0  [40] , A. brassicicola inoculum at 10 4 conidia per mL did not affect seed germination of Col-0 (Figure  4 ).
brassicicola in our RNA-seq dataset. In addition, some mutants altered in mucilage composition and structure such as cesa5  [51] , mum2  [52] , csla2  [53]  and gl2  [54]  were included. These latter mutant lines have impairments in genes that were shown to be repressed by A. brassicicola and have a structural and/or biochemical contribution to the interface contact between the seed tissue and the pathogen, making them good candidate genes for the entry of necrotrophic fungus into plant/seed tissues. Germination rates of healthy and A. brassicicola-infected seed lots were first determined at 3, 6 and 10 DAS in the mutants impaired in defense pathways (Figure  4 ). As previously described for the WT Col-0  [40] , A. brassicicola inoculum at 10 4 conidia per mL did not affect seed germination of Col-0 (Figure  4 ).
Interestingly at 3 DAS, a high enrichment index in the DEGs assigned to the cellular response to hypoxia was observed. The contribution of this pathway in plant pathogen interaction is increasingly documented  [59] [60] [61] [62]. Hypoxia or pathogen responses act through ERF-VII (ethylene response factor VII) and WRKYs (WRKY transcription factors) involved in oxygen sensing and signaling. Nearly three quarters of these genes described to be related to both hypoxia and pathogen response in Arabidopsis  [61]  was found up-regulated in our RNA-seq dataset and particularly in germinating seeds (Supplementary Table  S1 ). It is reported that both fungal pathogen and host are able to experience hypoxia during pathogenesis  [62, 63]. The response to low oxygen involves ethylene pathway factors and leads to competition with the pathogen for oxygen.