dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This research received funding from the  German Federal Ministry of Education and Research (RACE Regional Atlantic Circulation and Global Change , grant no. 03F0729C ). The model integrations were enabled by the provision of extensive computing resources on the HPC-systems JUWELS at the  Jülich Supercomputing Centre (JSC)  in the framework of the  Earth System Modelling Project  (ESM) and at the  North German Supercomputing Alliance (HLRN). We wish to thank the  DRAKKAR group  for their continuous support in the model development.
Code availability The NEMO code used for the numerical simulations is available at https://forge. ipsl. jussieu. fr/nemo/svn/NEMO/releases/release-3. 6. Our experiments are based on revision 6721. Code modifications and namelists, as well as further information necessary to reproduce the present model simulations, are available through GEOMAR at https:// hdl. handle. net/20. 500. 12085/44e539eb-5496-4298-bda5-10a4d655d34f (ref. 57).
Since the number of drugs based on natural products (NPs) represents a large source of novel pharmacological entities, NPs have acquired significance in drug discovery. Peru is considered a megadiverse country with many endemic species of plants, terrestrial, and marine animals, and microorganisms. NPs databases have a major impact on drug discovery development. For this reason, several countries such as Mexico, Brazil, India, and China have initiatives to assemble and maintain NPs databases that are representative of their diversity and ethnopharmacological usage. We describe the assembly, curation, and full chemoinformatic evaluation of the content and coverage in chemical space, as well as the physicochemical attributes and chemical diversity of the initial version of the Peruvian Natural Products Database (PeruNPDB), which contains 280 compounds.
Also, the amount and nature of experimental evidence published on active compounds are still limited  [18] , and most of the current studies reported crude medicinal activities, while potentially active compounds have been isolated only from a few numbers of plants  [19]. Computer-aided drug design (CADD), one of the key approaches to modern pre-clinical drug discovery, can be defined as computational methods that are applied to discover, develop, and analyze drugs and active molecules  [20]. Among the key approaches that comprise CADD, virtual screening is one of the major contributors to CADD since it stands as a contemporary approach to the experimental in vitro high-throughput screening (HTS) for hit identification and optimization  [21]. Integrating CADD approaches to curated databases, which are described as a wellorganized collection of data in any field, the drug development process may be sped up and cost reduced.
[22]. Considering this, large databases containing NPs from various data sources have been released, such as the COlleCtion of Open Natural prodUcTs (COCONUT), which contains 406,076 unique "flat" NPs, and a total of 730,441 NPs where stereochemistry has been preserved  [23] ; and the LOTUS initiative, which has 750,000 referenced structure-organism pairs  [24]. Also, several NPs compound databases from particular geographical locations have been assembled, such as the Traditional Chinese Medicine (TCM) Database@Taiwan database containing approximately 58,000 molecules  [25] ; the Indian Medicinal Plants, Phytochemistry and Therapeutics 2. 0 (IMPPAT 2. 0) which contains more than 10,000 phytochemicals  [26] ; and the AfroDB which is composed of around 1000 NPs  [27]. Likewise, some countries in Latin America have published their own public NPs databases such as the NuBBEDB from Brazil which contains more than 2000 NPs  [28] , and BIOFACQUIM from Mexico, which contains a total of 531 molecules  [29].
Furthermore, NPs databases had been used as a repository to identify several promising candidates to be considered for further development for the treatment of diseases  [30] , such as Chagas disease  [31, 32] , Tuberculosis  [33] , Leishmaniasis  [34, 35] , Schistosomiasis  [36] , and COVID-19  [37]. The present work introduces the first version of the Peruvian Natural Products Database (PeruNPDB), describing its assembly, curation, and chemoinformatic characterization of molecular diversity and coverage in chemical space. The database is freely available at the web-interface PeruNPDB Explorer (https://perunpdb. com. pe/). We anticipate that the PeruNPDB will make it possible to conduct additional virtual screening tests to create innovative pharmacological entities and other biotechnological approaches and serve as a resource for information on conservation guidelines.
Search strategy, study selection, and data extraction A systematic review search strategy to examine the literature for studies describing compounds from Peruvian NPs was adapted from  [38]. Whereas PubMed, the main database for the health sciences, maintained by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM), is a database that contains about 32 million citations, belonging to more than 5,300 journals currently indexed in MEDLINE  [39] ; it provides uniform indexing of biomedical literature, the Medical Subject Headings (MeSH terms), which form a controlled vocabulary or specific set of terms that describe the topic of a paper consistently and uniformly  [40]. Firstly, to find terms associated in the literature with Peruvian NPs, the MeSH terms "Peru" AND the "Natural Products" were employed in a search carried out at the PubMed database (https://pubmed.
ncbi. nlm. nih. gov/), (last searched on 10 June 2022), though the results were plotted into a network map of the co-occurrence of MeSH terms in the VOSviewer software (version 1. 6. 17)  [41] , which employs a modularity-based method algorithm to measure the strength of clusters  [42]. The resultant cluster content was analyzed to select relevant studies associated with Peruvian NPs. Three phases went into selecting the studies. First, papers written in languages other than English, copies of articles, reviews, and meta-analyses were disregarded. The highly relevant full studies were then retrieved and separated from the papers with a title or abstract that did not provide enough information to be included. Next, the titles and abstracts of the publications chosen through the search approach were visually evaluated.
PeruNPDB assemble and molecular properties calculation The simplified molecular-input line-entry system (SMILES)  [43]  of compounds previously described in the NPs selected in the previous step were searched and retrieved from PubChem  [44] , DrugBank  [45] , or ChEMBL  [46]  servers, while for unavailable compounds the ChemDraw tool  [47]  was employed to generate the SMILE notation. Moreover, the Osiris DataWarrior v05. 02. 01 software  [48]  was employed to generate the dataset's structure data files (SDFs). This followed the uploading to the Konstanz information miner (KNIME) Analytics Platform  [49] , where the "Molecular Type Cast", and the "RDKit Structure Normalizer" KNIME nodes were employed to curate the chemical structures on the dataset. Moreover, for every compound in the dataset, the classification system for describing small molecule structures is described based on NP Classifier  [50] , which employs a biosynthetic ontology that is specific to natural products; or ClassyFire  [51]  which is a general classification system for small molecules that are based on the ChemOnt ontology, was employed.
The KNIME's "RKDit Descriptor Calculator" node was employed to calculate six physicochemical properties of therapeutic interest, namely: molecular weight (MW), octanol/water partition coefficient (clogP), topological surface area (TPSA), aqueous solubility (clogS), number of H-bond donor atoms (HBD) and number of H-bond acceptor atoms (HBA) of the PeruNPDB, while the statistical analysis was done within the GraphPad Prism software version 9. 4. 0 (673) for Windows, GraphPad Software, San Diego, California USA, www. graphpad. com, by calculating the mean, median, standard deviation, and the coefficient of variation of the calculated properties. Box-and-whisker plots showing, the maximum and minimum values were generated for visualization, and the One-way ANOVA followed by Dunnett correction for multiple comparisons test was employed to evaluate the differences between the datasets.
Additionally, the Tanimoto similarity score was calculated for clustering the compounds, while the atom-pair-based fingerprints of the compounds were obtained using the "ChemmineR" package  [55]  in the R programming environment (version 4. 0. 3)  [56] , a heatmap was generated for visualization. The same procedure was employed in the reference datasets: AfroDB  [27] , BIOFAQUIM  [29] , and NUBBEDB  [28]  retrieved from the ZINC20 database  [57].
Global diversity: consensus diversity analysis Since chemical diversity strongly depends on the structure representation, it is reasonable to consider multiple representations for a complete global assessment. The consensus diversity (CD) plots have been proposed as simple two-dimensional graphs that enable the comparison of the diversity of compound data sets using four sets of structural representations: the molecular fingerprints, scaffolds, molecular properties, and the number of compounds  [58]. The multiple-variable plot was generated by GraphPad Prism software version 9. 4. 0 (673), whereas the y-axis represents the area under the cyclic system recovery curve  [59] , the x-axis, represents the median of the fingerprint-based diversity computed with Molecular Access System (MACCS) keys (166bits) and the Tanimoto coefficient  [60] , the bubble color represents the molecular properties of pharmaceutical interest, and the bubble size represents the number of compounds for each database.
Drug-likeness The Osiris DataWarrior v05. 02. 01 software  [61]  was employed to calculate the drug-likeness score of the compounds from the PeruNPDB; the calculation is based on a library of ~5300 substructure fragments and their associated drug-likeness scores. This library was prepared by fragmenting 3,300 commercial drugs as well as 15,000 commercial non-drug-like Fluka compounds  [61]. Frequency distribution of the obtained scores was performed at GraphPad Prism software version 9. 4. 0 (673) for Windows, GraphPad Software, San Diego, California USA ( www. graphpad. com), and plotted into stacked bar plots. Furthermore, the Lipinski Rule-of-5 (Ro5) is a set of four rules (logP, MW, and H-bond donor and acceptor cut-offs) for drug-likeness and oral bioavailability derived from a subset of 2,245 drugs  [62].
PeruNPDB assemble In the present study, the assembly of the PeruNPDB, followed by its chemoinformatic characterization on molecular diversity and coverage of the chemical space was performed; to select the studies from which the compounds will further retrieve, a search with the MeSH Terms "Peru" AND "Natural Products" was performed in the Pubmed database, followed by the construction of a network map of the co-occurrence of MeSH terms. The search resulted in 399 published papers between 1950-2021-, whereas establishing the value of five as the minimum number of occurrences of keywords, a map with 194 keywords that reaches the threshold was constructed (Figure  1A ). In the analysis of the map, it is shown that six main clusters were formed, while terms such as "Plant Extracts", "Plants, medicinal", "Phytotherapy", "Ethnopharmacology", "Ethnobotany", "Plants stems", "Plants bark", and "Seeds", which are associated with NPs were observed in the first cluster (red color).
Also, terms such as "Peru", "Humans", "Animals", and "Male", were recurrent terms. Although using the eligibility criterion established, 47 articles were selected which showed a 2000-2021-year range, and terms such as "Flavonoids", "Sesquiterpenes", and "Anthocyanins", were recurrent terms (Figure  1B ). Also, bibliographic data extracted from the selected articles analyzed: the "Journal of Agricultural and Food Chemistry", the "Journal of Ethnopharmacology", "Phytochemistry", and "Planta Medica" where the main peerreviewed journals were the studies describing compounds extracted from Peruvian NPs were published (Figure  1C ). Furthermore, while retrieving the SMILES of the compounds from PubChem, DrugBank, and ChEMBL, it was observed that 242 structures were found in the repositories and that 38 needed to be generated in the ChemDraw tool.
Molecular properties Six physicochemical properties were calculated for all compounds in PeruNPDB and plotted into box plots, which include the distribution of the same properties of the three reference datasets, retrieved from the ZINC20 database (Figure  4 ). To compare the results of the datasets, the coefficient of variation (CV) was calculated. which represents the ratio of the standard deviation to the mean and is considered a useful tool to statistically compare the degree of variation from one dataset to another  [53]. Besides the results of the HBA, in which NuBBEDB obtain the highest CV (123. 2%), the PeruNPDB showed the highest CV in MW, clogP, TPSA, clogS, and HBD with 46. 58, 84. 49, 112. 8, 50. 08 and 83. 84%, respectively. Still, the results from TPSA, clogP, clogS, and HBD showed high statistical differences compared to AfroDB, BIOFAQUIM, and NuBBEDB, while showed no statistical difference in HBA results compared to the AfroNP database (Figure  4 ).
Diversity analysis The heatmap generated using the Tanimoto score matrix and the atom-pair-based fingerprints show that there is a similarity between the structures of the compounds of the PeruNPDB, AfroDB, BIOFAQUIM, and NuBBEDB (Figure  5C ). Also, a consensus diversity plot was used to evaluate the diversity of the PeruNPDB dataset, based on molecular fingerprints, scaffolds, and physicochemical properties. The Euclidean distance of the scaled properties was used to compute the property-based diversity of the PeruNPDB, AfroDB, BIOFAQUIM, and NuBBEDB databases. Data points on a continuous color scale are used to represent the values on the color CD plot. Darker colors signify less diversity, but brighter colors signify more diversity. Finally, different point sizes are used to illustrate how large or tiny the databases are, with smaller data points indicating databases with fewer molecules.
Drug-likeness Druglikeness assesses qualitatively the chance for a molecule to become an oral drug concerning bioavailability and is established from structural or physicochemical inspections of development compounds advanced enough to be considered oral drug candidates  [63]. To assess the "druglike" profile of the compounds from the PeruNPDB two approaches were performed; firstly, the frequency distribution of the drug-likeness score was analyzed, and the results showed that besides the differences in the number of compounds compared in both datasets a similar distribution is observed among the compounds is observed (Figure  7A ). , In the second approach, the number of violations to Lipinski's Ro5 was analyzed and the results showed that compounds with at least one violation represent the 85.
82 and 76. 35% of the FDA and PeruNPDB datasets, respectively (Figure  7B ). Also, the visual representation of the chemical space as PCAs (Figure  7C ) and t-SNE (Figure  7D ) indicates that some of the NPs are distributed in the same space as the already approved drugs. The findings imply that because the compounds in PeruNPDB have chemical structures like those of approved medications, they can be used in virtual screening to find possible lead compounds or points for further optimization.
Discussion Peru has exceptionally high biodiversity, with numerous endemic species of mammals, reptiles, amphibians, flowering plants, and ferns, which is why has been described as a 'megadiverse' country  [64, 65] , but worldwide hotspot analysis for potential conflict between food security and biodiversity conservation points out Peru as a region that is especially at risk of biodiversity loss due to agricultural expansion  [66]. Thus, the conservancy of biodiversity can be considered important since historically NPs have played a key role in drug discovery, especially for illnesses such as cancer, cardiovascular and infectious diseases  [67] , while the growing interest in NPs and their application is evidenced by a growth of the number of published databases of NPs, and collections of structures from various organisms, geographical locations, targeted diseases, and traditional applications  [68].
According to the data, there are fewer compounds identified in the PeruNPDB than in AfroDB, BIOFAQUIM, and NuBBEDB, but the chemical diversity is also higher. Of the 280 compounds characterized, 95% came from plant sources, and 5% came from animal sources. But in the BIOFACQUIM and NuBBE databases as well as plant sources, compounds derived from fungi, propolis, bacteria, and marine organisms are also described. Furthermore, the Peruvian marine biodiversity hotspot located on the northern coast has been predicted to hold 501 species, 270 genera, and 193 families  [79] , as marine natural products have shown an interesting array of diverse and novel chemical structures with potent biological activities  [80] , which includes: Cephalosporin C an antibiotic derived from marine fungi Cephalosporium  [81] , Eribulin an anticancer drug derived from halichondrin B from the natural Japanese marine sponge Halichondria okada  [82]  and the antiviral, isolated from sponge Tethya crypta, nucleoside Ara-A  [83].
Conclusion Here we present the first version of PeruNPDB, a compound database of NPs from Peru that includes 280 compounds from plant and animal sources. PeruNPDB was constructed curated, and maintained by the Computational Biology and Chemistry Research Group from the Universidad Catolica de Santa Maria, and it is freely accessible through the website https://perunpdb. com. pe/. The PeruNPDB was envisioned as a tool for virtual screening, identifying promising compounds, serving as a springboard for further biotechnological products, and providing suggestions for conservation policies. The chemoinformatic characterization and analysis of the coverage and diversity of PeruNPDB in chemical space suggest broad coverage, overlapping with regions in the drug-like chemical space. The database contains an identification code (ID), the chemical name, bibliographic reference (name of the journal, year of publication, and DOI number), kingdom, genus, and species of the natural product, SMILES notation, and classification of the natural product.
Markers of Temozolomide Resistance Are Found in Recurrent Glioblastoma Tumors To validate the clinical relevance and identify a common pattern in the ADA phenotype, we conducted an in silico study using the GLASS database to investigate the expression of ADA temozolomide-resistant markers in recurrent glioblastoma samples. We considered all subpopulations (pseudo-bulk) to determine the overall markers of temozolomide resistance (Table  S4 ). As shown in Figure  4A , we found that 37 genes (18 upregulated and 19 downregulated) were commonly shared between GS359 and GS789 (Table  S5 ). While these genes may not be directly implicated in a particular biological process, the upregulated genes exhibited significant enrichment in cellular components such as collagens, junction complexes, synaptic membranes, filopodia and lamellipodia protrusions, adhesions, and ribosomes (Figure  4B ).
On the left, the network graph is related to the upregulated genes, while on the right the network graph refers to the downregulated genes. (C) Volcano plot representing the differentially expressed genes between primary and recurrent glioblastomas derived from the GLASS dataset. Positive log2 fold changes refer to the upregulation in the recurrent tumors compared to the primary glioblastomas. The overlapping significant upand downregulated genes with temozolomide-resistant (T2-TMZ) GS359 are displayed in the white boxes. (D) Venn diagrams displaying the amount of overlapping up-(left) and downregulated (right) genes between recurrent and temozolomide-resistant (T2-TMZ) GS359, and the percentage of the genes related to the two dominant clusters. (E) Volcano plot representing the differentially expressed genes between primary and recurrent glioblastomas derived from the GLASS dataset.
Bulk RNA Sequencing and Data Analysis Bulk RNA sequencing was performed on 21 primary glioblastoma cell cultures prior to treatment. The RNA library preparation and sequencing for all cell cultures was carried out as previously reported  [40]. Briefly, a total of 2 µg of RNA was isolated from the cell cultures, using KAPA stranded mRNA-Seq kit (Kapa Biosystems, Nazareth, Belgium). The sequencing was single-end 50 bp performed on an Illumina HiSeq4000. Before mapping, the optical duplicates and adaptors were removed with Clumpify v. 37. 28 and Fastx clipper v. 0. 0. 13, respectively. The quality of the reads was checked with FastQC v. 0. 11. 4. Next, the reads were mapped to the human reference genome GrCh38 with STAR v. 2. 6 and gene expression matrices were generated with HTSeq v. 0. 10. 0.
The raw counts were normalized using the R package EdgeR  [41]. The counts were filtered out for the lowly expressed genes and normalized using the TMM method in log2 scale. Batch effects were removed using the ComBat-seq package  [42]. The enrichment pathway analysis was carried out with the Gene Set Enrichment Analysis (GSEA) v. 4. 1. 0 software, using the Reactome database  [43]. The correlation of the genes with the phenotype were ranked by their signal-to-noise ratio. Following the GSEA guidelines, FDR q-values < 0. 25 were considered significant.
Single-Cell RNA Sequencing Data Analysis The downstream analysis of the scRNAseq data was performed in R using the Seurat package (v4. 0. 2)  [46]. Each single-cell RNA sequencing dataset was independently analyzed, resulting in four distinct datasets. Initially, we filtered out low-quality cells. Specifically, cells containing 2500-10,000 features and less than 30% mitochondrial genes were selected. Subsequently, we applied data normalization using SCTransform, integrated all conditions including different time points and treatments, and performed cell cycle regression as implemented in Seurat. To identify cell clusters, we used Seurat's FindClusters function on the integrated samples, and used UMAP for their visualization. Then, we identified differentially expressed genes of the clusters by applying the FindAllMarkers function with a non-parametric Wilcoxon rank-sum test (Bonferroni adjusted p-value < 0.
05), aiming to pinpoint markers of drug response. The differential expression analysis was conducted separately for individual clusters as well as for merged (pseudobulk) clusters. The subpopulations were annotated using scCATCH package, using "Glioma" and "Glioblastoma" as type of human cancer tissue reference  [47]. All the genes derived from the differential gene expression analysis identifying the clusters were used to create a ranked list that has been used as the input for the Gene Set Enrichment Analysis (GSEA) v. 4. 1. 0 software  [43]. GSEA was run using a module containing the signatures of the GBM cell states identified by Neftel et al. [10]. The differentially expressed genes identifying the drug treatments and time points response having an adjusted p-value < 0. 05 were used to run the gene enrichment analysis for gene ontology (GO), biological processes (BP), or cellular components (CC).
The network GO analysis was performed using the enrichGO and emapplot function of the clusterProfiler package  [48]. The Gliovis webtool (http://gliovis. bioinfo. cnio. es/, accessed on 15 July 2021) was used to perform the survival analysis, choosing the TCGA-GBM RNA-seq dataset with "high vs. low" cut-off parameter  [49]. The CNV were inferred using the package inferCNV, using the astrocytes derived from the study GSE171684 as normal cells  [50, 51]. The trajectory analysis and pseudotime calculation were performed in R using the package dyno, utilizing the Monocle DDRTree inference method  [52].