dataesr/scientific-paragraphs-categorization huggingface dataset:
In fact, comparisons of the photorates of HNC included here and those of HCN in the database reveal that HNC is slightly less prone to destruction under VUV. This places recent results by  Bublitz et al. (2022)  into some perspective as they found that the HNC/HCN ratio is anticorrelated with radial distance from the central star and, furthermore, it appears to be anticorrelated with UV emission over four orders of magnitude in incident flux. Hence, it is very important to characterize these photorates so that the HNC/HCN ratio could still be reliably used for regions experiencing harsher radiation fields, such as different layers of protoplanetary disks  (Long et al. 2021). With the recent detection of DME in a protoplanetary disk  (Brunken et al. 2022) , it is clear that accounting for photorates is presenting a new challenge for models.
This kind of separation is already available in the database for abundant molecules such as H 2 O and NH 3  (Heays et al. 2017 ), but significant progress has already been made in measuring absolute branching ratios of different photodissociation processes in small molecules. Namely, the branching ratios of several fragmentation channels from predissociative Rydberg states have been measured in CO  (Gao et al. 2011a (Gao et al. , 2012 (Gao et al. , 2013 (Gao et al. , 2020;; Shi et al. 2018; Chi et al. 2020) , N 2  (Gao et al. 2011b; Song et al. 2016; Shi et al. 2017) , and CO 2  (Lu et al. 2014). Besides molecular complexity, interstellar sulfur chemistry has been receiving more attention recently, particularly in the light of recent detections of numerous complex sulfur-bearing molecules in the ISM  (Fuente et al.
1995) , in volcanic plumes on Io  (Spencer et al. 2000; Jessup et al. 2007) , and cometary comae  (Ahearn et al. 1983; Kim et al. 2003). The photodissociation and photoionization cross sections of CS have also been updated. Implementing the photodissociation cross sections computed by  Xu et al. (2019)  requires bringing up the role resolution plays when it comes to interpolating measured and computed cross sections for numerical integrations. Throughout this paper and presented in the database, all cross sections and radiation fields are computed on a 0. 001 nm grid. The calculated cross section of CS, however, contains a myriad of finely resolved spectral features with FWHM that could only be fully captured using a resolution better than 0. 00004 nm. Hence, some spectral features were left out by using a larger grid which resulted in slightly smaller photorates (smaller than 10%) which is still better than the 20% accuracy limit required for an A+ rating.
Conclusions The Leiden VUV cross section database has been updated and expanded with new photodissociation and photoionization cross sections of molecules of varied astronomical interest, accompanied by uncertainty estimates. The cross sections are used to compute rates of photodissociation and photoionization in several different radiation fields and are compared with previous database entries where appropriate. Many of the new cross sections have been obtained thanks to experimental and theoretical advances which have been detailed. Thanks to these advances, the newly updated cross sections generally provide excellent constraints (generally better than 30%) to the computed photorates. Also accompanied and updated in the database are photorates computed assuming dust and line shielding and shielding functions that are suitable for use in astrochemical models that account for VUV photoprocesses, which are not easily parametrized.
Acknowledgements. Thanks to  Alan Heays  for assistance with the database update and computations, and  Arthur Bosman  for useful discussions. Thanks to  Séverine Boyé-Péronne ,  Bérenger Gans , and  Jean-Christophe Loison  for providing the photoionization efficiencies and threshold photoelectron spectra of CH, C 2 , C 2 H, C 2 H 3 , C 2 H 5 , HNC and SiH. Thanks to  Gustavo Garcia  for the photoionization efficiencies and the separated TPES of l-C 3 H, c-C 3 H, HC 3 H, and c-C 3 H 2. Thanks to  Zhongxing Xu  and  Kyle Crabtree  for providing the photodissociation cross sections of CS. Thanks to  Iouli Gordon ,  Robert Hargreaves , and  Fran Skinner  for providing the photoabsorption cross sections of S 2. This work was supported by the  Programme National "Physique et Chimie du Milieu Interstellaire" (PCMI)  of  CNRS/INSU  with  INC/INP  co-funded by  CEA  and  CNES.
In this context, the goal of this study was to perform a systematic evaluation of different network-based approaches that can be applied to uncover the mechanisms of drug-induced toxicity in a systematic manner. To accomplish this goal, we selected a well-characterized drug-induced adverse effect in terms of mechanistic description to be used as a benchmark for comparison of the results obtained (liver steatosis as a result of valproic acid treatment). The interpretation of the results of a network-based approach usually requires analysis and functional interpretation of a large set of genes, which can be facilitated by gene over-representation or enrichment analysis using a variety of functional annotation databases. However, the results of such enrichment analysis can also be cumbersome and tedious due to the large number of statistically significant terms that can be obtained.
A manual review of the genes involved in each AOP event (Molecular Initiating Event(s), Key Event(s) or Adverse Outcome(s)) was performed. Part of the geneevent association was extracted from the bibliography  (Aguayo-Orozco et al. , 2019) , and the rest of the associations were extracted either from the Event title (when the gene is explicitly named in the KE) or from the bibliography provided in the AOPWiki database.
Human interactome The human protein interaction network or interactome was obtained from the Multiscale Interactome repository  (Ruiz et al. , 2021)  (https://github. com/snap-stanford/multiscale-interactome, file 3_protein_to_protein. tsv), which integrates data from seven databases. The interactome includes physical interactions among human proteins supported by experimental evidence. Indirect and genetic interactions between proteins and self-interacting proteins were not included  (Ruiz et al. , 2021). The MI Score from IntAct (a quantitative estimation of the confidence for a given interaction among proteins) was assigned to the edges of the network, resulting in a weighted, undirected interactome. The protein interactome network comprising a total of 17,660 proteins and 387,626 edges was used as a network scaffold for the different network-based approaches considered in this study.
Selection of VPA targets and steatosisrelated genes The human protein targets of Valproic Acid (VPA) were obtained from ChEMBLv29  (Davies et al. , 2015; Mendez et al. , 2019) , Comparative Toxicogenomics Database (CTD) (06/01/2022)  (Davis et al. , 2023) , Papyrus (Version 2, 01/11/2022)  (Béquignon et al. , 2023) , DrugBank (04/01/2022)  (Wishart et al. , 2018) , and DrugCentral (10/03/2022)  (Ursu et al. , 2016). For ChEMBLv29, we selected associations with type of target "SINGLE PROTEIN" and discarded all associations with potential errors in their validations (data_validity_column from ChEMBLv29), confidence scores below 7 (the confidence score value reflects both the type of target assigned to particular assay and the confidence that the assigned target is the correct one for that assay), and non-active compound (labels not active, and inactive).
, 2019)  using customized gene sets from DISGENET plus v17. 4 (https://www. disgenetplus. com/) and the Comparative Toxicogenomics Database (CTD). Clusters were selected for downstream analysis by their annotation with at least one of the liver steatosis concepts (Diffuse hepatic steatosis, Fatty Liver, Macrovesicular hepatic steatosis, Microvesicular steatosis, Nonalcoholic Fatty Liver Disease, Nonalcoholic Steatohepatitis, and Steatohepatitis), association with Valproic acid (CTD:D014635), and inclusion of VPA targets. Clusters were prioritized using CRANK (version 03/08/2017)  (Zitnik et al. , 2018) , a tool that evaluates the robustness and magnitude of structural features of each module such as the connectivity within and between clusters, and then combines these features into a score.
Note that other experimental designs could be applied (see below in the iPath section). GUILD was run according to developer recommendations using NetCombo (http://sbi. imim. es/web/index. php/research/software/guildsoftware). All genes with a prioritization value ≥0. 8 were selected for the gene enrichment analysis. The seed nodes usually receive the higher scores, followed by other prioritized genes. Note that prioritized genes can be directly or indirectly connected to the seed gene through more than one linker node  (Lenselink et al. , 2017).
We modified the MSI approach to incorporate new datasets, and edge weights and to apply it to drug toxicity (manuscript in preparation). The heterogeneous network underlying the MSI integrates different node types and their interactions: proteins, a full hierarchy of biological processes, drugs, diseases, and their symptoms and manifestations. From the original MSI network implementation (https://github. com/snapstanford/multiscale-interactome), the data on compound-target and gene-disease associations were updated. Chemical-protein interactions were obtained from ChEMBLv29 database (18/02/2022)  (Davies et al. , 2015; Mendez et al. , 2019)  using the assay and activity tables, which include experimental data from the literature. The tables were combined using the "Assay ID", and the information of targets and drugs through "Molregno ID".
In addition, we included the drug mechanism table, a manually curated dataset that integrates the putative therapeutic targets. The studies that evaluate the effect of compounds on human proteins were selected. Then, the studies focused on protein complexes, chimeric proteins, protein-nucleic acid complexes, protein families, and those studies which evaluate the drug against a protein-protein interaction were removed. All associations with errors in their validations, confidence scores below 7, potential duplicates, uncertain compound activities (according to the following labels: Inconclusive, not active, inactive, not determined, indeterminate, ineffective, lack of solubility, no compound available, no compound detectable, no data, non-valid test, not assayed, not detected, qualitative measurement, precipitate, too insoluble, unable, approximate value, unspecified, uncertain, insoluble, not evaluated, TDE, not tested) were removed.
gene_info. gz and included in our dataset using the NCBI Gene ID. To obtain universal compound IDs, the ChEMBL IDs were converted to Inchi keys using the chembl_29_chemreps. txt file downloaded from ChEMBL (https://ftp. ebi. ac. uk/pub/databases/chembl/ChEMBLdb/latest/). The ChEMBL ids were maintained for those compounds without the Inchi key. After harmonizing the IDs, we filtered out the duplicated interactions. Finally, we selected all chemical-target interactions involving proteins included in the 3_protein_to_protein. tsv file (human interactome file). The Gene-Disease Association (GDA) data was obtained from DISGENET plus (v17. 4) (https://www. disgenetplus. com/). We only considered gene-disease associations whose proteins were included in the protein interaction dataset (human interactome file).
We modified the diffusion profile algorithm by incorporating confidence scores on the edges between nodes. The MSI relies on a biased random walk algorithm to propagate the effect of a drug or a disease through the network, using optimized edge weights (w t ). The edge weights encode the relative importance of nodes of different types. M is the biased transition matrix, and each element M ij denotes the probability p ij that a random walker jumps from node i to node j rather than to another adjacent node of type t. We introduced confidence scores (u ij ) of the edges between nodes in p ij : for interactions among proteins, the MI score from IntAct; for chemical-protein associations, the pChembl; and for disease-protein associations, the DISGENET score. In the case of the associations between biological process-biological process and biological process-protein, we assigned a score of 0.
We customized the annotation dataset using the GMT g:Profiler file (gprofiler_full_hsapiens. ENSG. gmt (date 11/ 10/2022)). The ENSEMBL IDs were converted to NCBI gene IDs using the https://ftp. ncbi. nih. gov/gene/DATA/GENE_INFO/ Mammalia/Homo_sapiens. gene_info. gz file. The genes without NCBI gene IDs were discarded. Finally, the file format of the custom annotation dataset, the gene list of the interactome, and the candidate genes were generated according to developer recommendations (https://bioconductor. org/packages/release/bioc/ vignettes/topGO/inst/doc/topGO. pdf). TopGO was executed using the Biological Process (BP) category from the Gene Ontology, discarding GO terms with less than 5 annotated genes. Then, GO terms with a Fisher test p-value below 0. 01 were selected as input for Revigo.
The comparison of gene sets and biological processes obtained from different network methods was performed in R version 4. 2. 0 using UpSetR  (Conway et al. , 2017)  to evaluate the overlap between datasets/subnetworks generated with each approach. We selected the candidate genes identified by at least two network-based approaches that were not directly associated with steatosis or VPA targets for further inspection. We refer to these genes as "novel candidate genes". We used the Human Protein Atlas (HPA)  (Uhlén et al. , 2015)  (https://www. proteinatlas. org/ about/download/rna_tissue_consensus. tsv. zip) to assess if these genes are expressed in the liver by selecting genes with an NX score >0 in liver tissue. Finally, the gene enrichment strategy (section "Gene enrichment analysis with GO Biological Processes") was applied to annotate the novel candidate genes using the Biological Processes terms from the Gene Ontology.
Clustering approach (MONET) The clustering of the interactome was performed with the K1 method from MONET, a tool resulting from the DREAM challenge on clustering approaches for disease module identification  (Tomasoni et al. , 2020). The clustering approach consists of grouping the genes from the network into modules that represent coherent biological functions. The Largest Connected Component of the interactome was used as a network scaffold (17,660 nodes and 387,626 edges). Once a network partition was obtained with the K1 method from MONET, 473 of 1,486 clusters with more than 5 genes were selected for downstream analyses. After the annotation with g:Profiler, we found 26 clusters associated with at least one of the following liver steatosis concepts: Diffuse hepatic steatosis, Fatty Liver, Macrovesicular hepatic steatosis, Microvesicular steatosis, Non-FIGURE 2 Workflow of the strategy followed in this study.
induced liver steatosis (Table  1 ). A set of 823 genes was obtained with a prioritization score ≥0. 8. From these 823 genes, 25 genes overlapped with VPA targets and 817 with liver steatosis-associated proteins (Table  2 ). Compared to other approaches, all genes provided by GUILD were already associated with VPA or liver steatosis. Finally, 24 of 33 genes from the liver steatosis AOP were recovered with the GUILD approach. By gene enrichment analysis a list of 1,529 biological processes with a p-value ≤0. 01 was obtained, the three biological processes with high p-values were also obtained with the MSI approach (negative/positive regulation of transcription by RNA polymerase II and positive regulation of gene expression (GO:000122, GO: 0045994, GO:0010628)). Following the analysis with Revigo, 1,017 of 1,529 biological processes were clustered in 253 modules, with 58% of them having ≥3 GO terms (Table  3 ).
After the gene enrichment analysis, 402 biological processes were obtained with a p-value of 0. 01 (Table  3 ). The most significant biological process was the xenobiotic catabolic process (GO:0042178) with a p-value of 1. 3e-13, which has been associated with fatty liver diseases  [47]  followed by the estrogen metabolic process (GO:0008210) (p-value 1. 8e-13). After the analysis with Revigo, 182 GO terms were clustered in 63 modules, where 46% of them had ≥3 GO terms (Table  3 ). Three clusters had 7 GO terms where the GO representative is associated with the neuronal action potential (GO:0019228), positive regulation of glycolytic process (GO:0045821), and regulation of heart rate by cardiac conduction (GO:0086091). Comparing the gene enrichment between the Steatosis AOP gene set and iPath, 50 of 178 GO terms annotated with the Steatosis AOP gene set were also annotated with the iPath approach.
Multiscale interactome approach (MSI) MSI relies on comparing the diffusion profiles of drugs and diseases that are obtained from the heterogeneous network, which integrates information on drug targets, disease-associated genes, protein interactions, and a hierarchy of biological processes. By comparing diffusion profiles, it is possible to prioritize the genes and biological processes that are relevant to a particular drug effect. By comparing the diffusion profiles of VPA and liver steatosis, the top-1000 genes and biological processes were selected based on their values of the Treatment Importance (TI) (further details in the section "Multiscale interactome (MSI)"). One thousand genes represent 0. 15% of each diffusion profile. Smaller sets of genes could be selected for downstream processing, but we decided to select a large number of genes to enable covering a large fraction of seed genes and to emulate situations of analysis of omics datasets where a large number of genes are obtained for further analysis.
, 2021). Due to the ability of the Steiner tree algorithm to connect the highest fraction of the genes used as seed, iPath recovers a comparatively smaller network and gene set which could explain the very low coverage of the benchmark dataset. MONET, on the other hand, has the potential of capturing every gene included in the network but the prioritization and selection steps condition the coverage of the benchmark dataset. MONET captures a smaller fraction of VPA targets and steatosis genes compared with the other approaches (with the exception of iPath for steatosis genes). The small fraction of VPA targets recovered might be due to the experimental design in the clustering approach, which started by selecting disease-enriched clusters. This step might result in losing clusters that contain drug targets but not disease genes.
Discussion This study aims at evaluating different network-based methods that can be applied to provide biological insights into how drugs lead to diseases. The approaches evaluated in this study are based on different network medicine assumptions  (Barabási et al. , 2011; Liu et al. , 2020)  and also differ in the experimental setting (Table  4 ). More concretely, each method accepts as input a different set of genes (a. k. a. Seed genes) and a scaffolding network and requires the definition of criteria for setting thresholds or prioritization rules. For instance, the method based on clustering (referred to as MONET) starts with a partition of the scaffold network into modules (note that different clustering methods could be applied to partition the network) and then requires the definition of criteria on how to select the relevant modules, which frequently require projecting a set of seed genes into the clusters to identify those clusters that contain the genes.
disgenetplus. com/) and the disgenetplus2r package (https:// medbio. gitlab. io/disgenetplus2r/). The width of the edges is proportional to the DISGENET plus score. network-based approaches and tools available, we selected 4 of them that differ in their principles and results for illustrative purposes. Moreover, most of them require consideration of the experimental design and the definition of thresholds and filtering strategies that are more relevant to the problem under study. Since there are no clear guidelines on how the experimental setting, selection of input data, and filtering criteria should be set, the user must consider all these aspects carefully before designing an Frontiers in Pharmacology frontiersin. org 13 experiment and bear in mind these aspects during the interpretation of the results.
Funding This work was partially funded by  IMI2-JU  grants, resources which are composed of financial contributions from the  European Union 's  Horizon 2020 Research and Innovation Programme  and  EFPIA  (GA:  777365   eTRANSAFE ), the  EU   H2020 Programme  (GA:  964537   RISKHUNT3R , which is part of the  ASPIS cluster) , and the  Austrian Science Fund/FWF , grant  W1232  ( MolTag ).
In these populations, the advantage of more advanced DL-based methods over more traditional signal-processing and machine-learning-based methods is reported to be minimal  (Balakrishnan et al. , 2021). The true advantage of these advanced methods may be more evident for the segmentation of WMH in subjects with relatively mild lesion load (< 5mL), as recently suggested  (Khademi et al. , 2021; Li et al. , 2022; Rachmadi et al. , 2018). However, even those studies evaluating their method in subjects with mild WMH burden with DL-based  (Khademi et al. , 2021; Rachmadi et al. , 2018)  or other approaches  (Ong et al. , 2022; Rachmadi et al. , 2020)  primarily use databases with 2D FLAIR acquisition with the slice thickness ranging from 3 to 5 mm, which precludes detection of small WMH not on the plane of acquisition.
To our knowledge, no study has explicitly optimized the segmentation performance in high-quality 3D FLAIR scans from healthy young-to middle-aged adults with very mild WMH burden. In the present study, we took advantage of the unique, large neuroimaging database of French university students called MRi-Share  (Tsuchida et al. , 2021). We first characterize small WMH visible on the 1mm isotropic 3D FLAIR scans in a subsample of 50 MRi-Share participants. Using the multi-shell diffusion-weighted imaging (DWI) also available in these subjects, we demonstrate that albeit their small size and relative sparsity, these small WMH found in young adults already exhibit altered white matter microstructural properties relative to normal-appearing white matter (NAWM). We then describe the development of a 3D Unet-  (Ronneberger et al.
, 2015)  based tool, trained with the manually delineated WMH in these subjects. It is based on the model we described previously  (Boutinaud et al. , 2021)  that detected perivascular spaces (PVS), another marker of covert cSVD  (Wardlaw et al. , 2013; Yu et al. , 2022) , in the same young subjects. Since we developed this tool in the context of the SHIVA project (https://rhu-shiva. com/), whose aim is to prevent cognitive decline and dementia through a better understanding of cSVD, we call our tool 'SHIVA-WMH' detector. Our objective was to create a ready-to-use tool that can chart the emergence and progression of WMH across the adult lifespan in multi-cohort studies with varying age ranges. Thus, in order to cover the full range of age-related WMH severity when tuning the performance of the SHIVA-WMH detector, we also used a publicly available MRI dataset from the MICCAI 2017 WMH segmentation challenge (MWC:  Kuijf et al.
, 2021) , a state-of-the-art, ensemble 2D Unet-based model that is currently the winner of the MWC, and 3) HPM  (HyperMapper; Mojiri Forooshani et al. , 2022) , another state-of-the-art 3D Unet-based tool with promising results in subjects with mild WMH burden. We present the performance metrics both at the voxel-and individual lesion cluster-level for each tool, and provide cohort by cohort analysis for performance comparison. Our SHIVA-WMH detector tool with pre-trained models is made openly available at (https://github. com/pboutinaud/SHIVA_WMH) to other researchers to encourage replication and further research into the earliest forms of WMH.