dataesr/scientific-paragraphs-categorization huggingface dataset:
Since ADA plays a major role in DNA turnover and nucleotide metabolism, ADA inhibitors have been extensively used for chemotherapeutic purposes in some types of cancers including breast cancer  [94, 95]. For this regard, arbutin was discovered to be a powerful inhibitor with a docking score of -7. 4 kcal/mol compared to the native ligand (2 -deoxyadenosine) with -6. 2 kcal/mol (Figure  4 ). Purine nucleoside phosphorylase (PNP, PDB ID: 1M73) was also identified as a target of arbutin, but its activity as a potential inhibitor was insignificant, which suggests that the cytotoxic activity of AHEE is not mediated via PNP inhibition and immune activation  [96]. A 1 , A 2A , A 2B , and A 3 are four G-protein-coupled receptor subtypes that interact with adenosine to mediate its physiological effects.
Adenosine appears to be associated with the development of tumors; nevertheless, it has been noted that cancer tissues have significant amounts of the substance  [97]. A 1 receptor (PDB ID: 5N2S) was found as a target for trimethoxyflavone. The latter was found to be a strong inhibitor. Docking score of trimethoxyflavone was -7. 9 kcal/mol, while that of PSB36 (1-butyl-3-(3-hydroxypropyl)-8-(3-noradamantyl) xanthine, a potent antagonist of A1 receptor, was -6. 0 kcal/mol (Figure  4 ). The A 2A and A 3 subtypes of adenosine receptors appear to be the most promising in terms of drug development, despite the fact that all adenosine receptors now have an increasing number of biological roles in cancers that are recognized. A 2A receptor activation in particular has immunosuppressive effects that lower anti-tumor immunity and promote tumor development.
This tendency has led to the suggestion that A 2A antagonists be added to cancer immunotherapeutic procedures in order to improve tumor immunotherapy. In this regard, trimethoxyflavone was regarded as an inhibitor due to its high affinity (-9. 0 kcal/mol) with A 2 receptor (PDB ID: 2YDO), in comparison with a known ligand (istradefylline, -8. 4 kcal/mol) (Figure  4 ). The inherent or acquired resistance of tumor cells to chemotherapeutic drugs with different chemical structures and modes of action is one of the major reasons why treatment for many malignancies fails  [97]. One of these processes entails the activation or overexpression of drug-export proteins called ATP-binding cassette (ABC) protein transporters, which lowers the levels of drug accumulation in the cell  [98, 99]. ABC transporter (PDB ID: 5NJ3) was found to be a specific target of trimethoxyflavone, but the latter substance had lower affinity (-8.
LC-MS/MS Profiling of AHEE Aliquots of the samples (80 mg) were extracted using the procedure described in  [114, 115] ; below is the procedure in detail: 1 mL of ethanol was added to the aliquot. The Eppendorf tube was vortexed and submerged for 60 min in a 45 • C sonicator bath. A Shimadzu Ultra-High-Performance Liquid Chromatograph (UHPLC, Nexera XR LC 40, Kyoto, Japan) with an MS/MS detector was used for the qualitative analysis (LCMS 8060, Shimadzu Italy, Milan, Italy). The Lab Solution software (ver. 5. 6, Kyoto, Japan) was used to manage the MS/MS, which used electrospray ionization. This program allowed for a quick transition from a low energy scan of 4 V (full scan MS) to a high energy scan (10-60 V ramping) during a single LC cycle. A total of 2. 9 L/min nebulizing gas flow, 10 L/min heating gas flow, 300 • C interface temperature, 250 • C DL temperature, 400 • C heat-block temperature, and 10 L/min drying gas flow were the settings chosen for the source.
The analysis used a mobile phase of acetonitrile and water + 0. 01 percent formic acid in a 5:95 (v/v) ratio, with no chromatographic separation. Mass spectral data acquisition was achieved under negative electrospray ionization (ESI-). A molecule was considered positive if its area under the curve was greater than that of the blank (information on the molecules' retention times and typical fragments in m/z are available in Figures  S1  and S2 ; typical fragments of the used standards are found in Supplementary File S2). The molecules were identified by comparing the typical fragment identified with those in our in-house-developed library of molecules.
Prediction of the Pharmacokinetic Properties and Toxicity of AHEE's Bioactive Compounds ADME (absorption, distribution, metabolism) profiles were assessed in silico via computational tools using the SwissADME online server (http://www. swissadme. ch/, accessed on 25 July 2022)  [116]  and pkCSM web-server (http://biosig. unimelb. edu. au/ pkcsm/, accessed on 25 July 2022)  [57, 117]. The estimation of LD 50 values, toxicity class, hepatotoxicity, carcinogenecity, immunotoxicity, mutagenicity, and cytotoxicity was carried out by the Protox II online tool (https://tox-new. charite. de/protox_II/, accessed on 28 July 2022)  [118]. Using the target proteins' PDB IDs, the crystallographic three-dimensional structures of the target proteins were obtained from the protein data bank (https://www. rcsb.
org/) (accessed on July 25 July 2022) and displayed using the Discovery Studio 4. 1 (Dassault Systems Biovia, San Diego, CA, USA) program. With the same tool, typical inhibitors, water molecules, and ions were also eliminated. Automated docking investigations were conducted using AutoDock Vina v1. 5. 6 software  [119]. The protein also acquired polar hydrogen bonds and Kollmann charges  [120]. The identified compounds in AHEE were retrieved from https://pubchem. ncbi. nlm. nih. gov/ (accessed on 25 July 2022) as "3D sdf" format and then converted using PyMol to pdb file format. Using MGL tools, three-dimensional PDBQT files of the protein and ligands were created. In order to generate the grid maps with the interaction energies on the basis of the macromolecule target of the docking investigation, AutoGrid grid, a part of Auto Dock, was employed.
The grid box used to represent the docking search space was expanded to accommodate the current binding site more accurately. Kcal/mol values for the ∆G binding energies were used to express the findings for the docked ligand complexes. Using Discovery Studio 4. 1, 2D molecular interaction schemes were created and protein-ligand binding interactions were examined. On the basis of the literature, we selected four antioxidant proteins for the prediction of antioxidant activity: lipoxygenase (PDB: 1N8Q), CYP2C9 (PDB: 1OG5), NADPH oxidase (PDB: 2CDU), and bovine serum albumin (PDB: 4JK4).
IV. COMPUTATIONAL DETAILS The present formalism has been implemented in the electronic structure package QuAcK 101 which is freely available at https://github. com/pfloos/QuAcK. We consider here only systems with closed-shell singlet ground states. Thus, the GW and GT calculations are performed by considering a (restricted) HF starting point and standard gaussian basis sets (defined with cartesian functions) are employed. Note that all quasiparticle energies which are obtained via Eq. (  13 ) are corrected in the same way. Finally, the infinitesimal η is set to zero for all calculations. The evGT and qsGT schemes have been also implemented but are not considered here. Although the dynamical correction is computed in the dTDA throughout, the zeroth-order excitonic Hamiltonian [see Eq. (  18 )] is always the "full" BSE static Hamiltonian, i.
85, 86, 114 The combination of GT and GW via the range separation of the Coulomb operator to avoid double counting of the low-order diagrams is also a promising avenue. Work along these lines are currently under progress. Finally, the unrestricted and spin-flip extensions of the present formalism are currently being developed. FIG. 3. Error with respect to FCI for the lowest singlet (left) and the lowest triplet (right) excitation energies of H 2 as a function of the internuclear distance R H-H (in Å) computed within the static schemes (BSE@G 0 W 0 and BSE@G 0 T 0 ) and the dynamically-corrected schemes (dBSE@G 0 W 0 and dBSE@G 0 T 0 ). The cc-pVTZ basis is employed for all calculations. Raw data are reported in supplementary material. gramme (Grant agreement No. 863481) for funding. This project has also received financial support from the CNRS through the 80|Prime program and has been supported through the EUR grant NanoX ANR-17-EURE-0009 in the framework of the "Programme des Investissements d'Avenir".
High-throughput sequencing (HTS) and sequence mining tools revolutionized virus detection and discovery in recent years and implementing them with classical plant virology techniques results to a powerful approach to characterize viruses. An example of a virus discovered through HTS is Solanum nigrum ilarvirus 1 (SnIV1) (family Bromoviridae), which was recently reported in various solanaceous plants from France, Slovenia, Greece, and South Africa. It was likewise detected in grapevines (Vitaceae) and several Fabaceae and Rosaceae plant species. Such a very diverse host association is atypical for ilarviruses, thus warranted further investigation. In this study, modern and classical virological tools were combined to accelerate the characterization of SnIV1. Through HTS-based virome surveys, mining of sequence read archive datasets, and literature search, SnIV1 was further identified from diverse plant and non-plant sources globally.
2022). Recently, mining of vast amounts of Arabidopsis thaliana publicly available sequence read archive (SRA) datasets uncovered a novel comovirus (family Secoviridae), Arabidopsis latent virus 1, which was demonstrated to be mechanically and seed transmitted, but causes no symptoms in A. thaliana,  (Verhoeven et al. 2022). A recent study on Prunus-associated luteoviruses (family Luteoviridae) also uncovered a new luteovirus through a search of SRA datasets  (Khalili et al. 2022 ). Many HTS-based discoveries of crop and non-crop viruses have also been reported  (Gaafar et al. 2020; Rivarez et al. 2022; Ma et al. 2019; Xu et al. 2017 ), but only a small subset of these studies biologically characterized the identified viruses  (Hou et al. 2020; Rivarez et al. 2021). Thus, there is a very wide knowledge gap concerning the biological properties of newly discovered plant viruses, which limits the evaluation of their economic and phytosanitary risks.
The general aim was to assess the global diversity and distribution and to characterize some biological properties of SnIV1 and, specifically, to answer the following questions: (1) Can the geographic distribution and associated hosts of SnIV1 be expanded using an HTS-based viromic survey of various plant species and by searching relevant SRA datasets? (2) What is the level of genetic diversity of SnIV1 relative to other related species? (3) Do isolates of SnIV1 show some form of phylogenetic structuring? (  4  For total RNA extractions performed in France, a previously described method  (Foissac et al. 2005 ) was used for leaves, stems, seeds, fruits, roots, pollen, and floral parts of S. villosum and for the pollen of S. nigrum prior to RT-PCR testing. Likewise, this protocol was used to extract total RNAs individually from inoculated test plants prior to RT-PCR testing.
2022). Additional SnIV1 primers targeting RNA 1 and RNA 3 segments were designed using OligoCalc  (Kibbe 2007)  and used in two-step RT-PCR reactions as previously described  (Marais et al. 2014) , to test for SnIV1 in different plant samples from France. In each RT-PCR assay, RNA extracts from SnlV1-positive samples were used as positive control, RNA extraction control and/or healthy plants as negative controls, and no template (water only) as blank control. Search for SnIV1 sequences in databases and the literature. Publicly available databases and the literature were searched for SnIV1 sequences to obtain additional SnIV1 genomes. Firstly, NCBI GenBank database release version (r. v. ) 250 that includes nucleotide, whole genome shotgun (WGS), transcriptome shotgun assembly (TSA) sequences, etc.
(Sayers et al. 2022 ) was searched using the BLASTn algorithm  (Altschul et al. 1997) , and with SnIV1 genomic segments (GenBank accession number (a. n. ) OL472060-OL472062) as queries. The search was performed on July 22, 2022. Percent identity and percent query coverage values were both set at greater than 90% as threshold to select significant BLASTn hits. Secondly, a search in global metagenomes, metatranscriptomes, or metaviromes was performed through a palmID search in Serratus (https://serratus. io/palmid)  (Edgar et al. 2022) , which analyzed and annotated in a database (palmDB) around 5. 7 million global SRA datasets deposited as of January 2021. The SnIV1 RNA 2 sequence (a. n. OL472061) which contains the RNA-dependent RNA polymerase (RdRp) domain needed for the palmID search was used as query.
A 100% RdRp 'palmprint'  (Babaian and Edgar 2021)  amino acid identity and an E-value less than 8. 4x10 -74 were used as thresholds to identify significant palmID hits. Third, published literature was searched using Google Scholar (https://scholar. google. com/) with the queries 'Solanum nigrum ilarvirus 1', 'grapevine-associated ilarvirus', or 'surrounding legume-associated ilarvirus', which are the first three virus names proposed for SnIV1. The search was performed on August 3, 2022. SnIV1 sequences that were not yet available in GenBank r. v. 250 were requested from the authors of the studies that identified the virus prior to their release in NCBI or as official publication. Assembly of SnIV1 genomes. A previously described bioinformatic pipeline was used for the reference-guided assembly of SnIV1 genomes from HTS data in this study  (Pecman et al.
2017; Rivarez et al. 2022). Using the pipeline that was run in CLC Genomics Workbench (GWB) version (v. ) 20 (Qiagen, USA), barcodes were removed from raw reads that were previously screened based on phred quality scores. Virus and virus-like reads and contigs were initially identified by mapping reads and contigs to the virus RefSeq database r. v. 212  (Sayers et al. 2022)  and by viral domain searches in contigs against the pFam v. 33  (Mistry et al. 2021 ). SnIV1 consensus genomes were reconstructed by mapping reads to the genome of a Slovenian isolate (a. n. OL472060-OL472062), with percent identity and genome coverage threshold set at ≥90%. Mapping profiles were visually inspected and read mapping depth (r. m. d. ) or on average, the number of times each locus in a reference genome is covered by mapped reads was noted.
The number of mapped reads, percent genome covered, and the presence of a complete set of open reading frames (ORFs) were noted, in reference to some recommendations for the detection of virus genomes in metagenomic data  (Roux et al. 2019; Simmonds et al. 2017). Genomes assembled from SRA datasets were deposited in GenBank as third party annotations. In parallel efforts, a previously described method was used to de novo assemble and annotate contigs from HTS data obtained from Belgian samples  (Buzkan et al. 2019). When needed, contigs representing SnIV1 genome segments were extended by iterative mapping of reads as implemented in Geneious Prime v. 2022. 1. 1 (Dotmatics, USA). A customized workflow  (Pecman et al. 2022 ) was used to assemble SnIV1 genomes from ONT MinION sequencing data of S.
villosum (inoculum) and inoculated N. benthamiana. The workflow was used for quality screening, barcode trimming, demultiplexing, visualization, and de novo assembly. Minimap2 v. 2. 24  (Li 2018)  as implemented in Geneious Prime v. 2022. 1. 1 (Dotmatics, USA) was used for mapping reads or contigs to viral RefSeq r. v. 212  (Sayers et al. 2022 ) and SnIV1 genome (a. n. OL472060-OL472062).
Multiple sequence alignments and recombination detection analyses. To examine the molecular diversity of SnIV1, nucleotide (nt) sequences were aligned using MUSCLE  (Edgar 2004 ) as implemented in MEGA X  (Kumar et al. 2018). To compare SnIV1 diversity with that of other ilarviruses, sequences of isolates of tobacco streak virus (TSV) and Parietaria mottle virus (PMoV) were retrieved from GenBank r. v. 250 and aligned similarly for further analyses. Prior to diversity and phylogenetic analyses, possible recombination events among the SnIV1, TSV, or PMoV sequences were checked using RDP v. 4  (Martin et al. 2015). A recombination event was considered significant if it has a p-val<10 -4 in at least four of the methods used (RDP, GENECONV, Bootscan, Maxchi, Chimaera, SiSscan, PhylPro, LARD, 3Seq) (de  Klerk et al.
RESULTS SnlV1 genome sequences obtained from HTS of different plant species. SnIV1 genomes were sequenced and assembled in four different HTS experiments (Table  2 ). Nanopore long read sequencing of rRNA-depleted total RNA from S. villosum and from N. benthamiana yielded high coverage SnIV1 genomes. In this experiment, r. m. d. ranged from 29x to 15,841x with 100% genome coverage in all three genome segments. Full genome sequences of TBRV2 were also recovered from both plants and are 90-95% identical to TBRV2 genomes deposited in GenBank from a previous study  (Rivarez et al. 2022 ) (Supplementary Fig. 1 ). This is the first detection of TBRV2 in a wild plant (S. villosum) and in France. However, TBRV2 was not identified from the HTS datasets of the other sequenced samples discussed below.
The presence of SnIV1 was also evaluated in pollen from a graft-inoculated S. nigrum plant (see details below) maintained in the greenhouse and in seeds from randomly sampled wild S. villosum plants that were similarly processed tested negative for SnIV1. In silico detection of SnIV1 in databases and in the literature. Information on sequences and existing records of SnIV1 were collected to gain a comprehensive picture of its global diversity and geographic distribution (Table  4 ). Aside from SnIV1 genomes deposited in GenBank database r. v. 250, SnlV1 was also detected through BLASTn homology searches in a publicly available TSA of hop (Humulus lupulus var. lupulus, family Cannabaceae) from Japan  (Natsume et al. 2015). So far, this is the only detection of SnIV1 sequences that are linked to samples from Asia and deposited in the GenBank database.
Furthermore, search of SRA datasets through palmID in Serratus  (Edgar et al. 2022 ) returned 80 datasets with RdRp 'palmprint' sequences that are 100% identical to that of SnIV1 (103 amino acid residues, E-value < 10 -74 ). These results included detections of SnIV1 in sequence datasets from China and the USA and several European countries. A literature search identified recent studies that detected SnIV1 sequences that were not yet available in GenBank r. v. 250. This search identified four new plant viromic or disease etiology studies, including two that detected the virus in South Africa, which represent the first reports of SnIV1 in the African continent  (Mahlanza et al. 2022; Bester and Maree 2022). The other two studies are viromic studies of peach in the USA  (Dias et al. 2022) , and an HTS study of symptomatic peppers (hybrid Arlequin F1) from Greece  (Orfanidou et al.
2022). The study from Greece demonstrated the mechanical transmissibility and infectivity of SnIV1 in N. benthamiana and in the same genotype of peppers. In total, 25 independent studies that detected SnIV1 sequences were identified, 15 of which were gathered through the palmID search. In terms of timing, the oldest SRA dataset with SnIV1 presence was released in 2013, and is a transcriptomic study of Medicago trunculata root nodules from France  (Roux et al. 2014) , while the most recent study concerns the viromic exploration of wild Solanum species  (Mahlanza et al. 2022 ) and apricots (P. armeniaca)  (Bester and Maree 2022)  in South Africa. The geographic origin of the biological samples from these studies spanned five out of the six habitable continents or 11 countries, including eight independent studies conducted in the USA.
Seventeen studies involved sequencing of plant samples, including seven that involved sequencing of members of Solanaceae family. Of the seven studies that utilized non-plant samples, four involved the sequencing of bee species (Apidae). These in silico searches highlighted associations of SnIV1 with 12 different plant species of economic importance (e. g. , crop, medicinal, ornamental, or fuel feedstock), six noncrop or wild plant species, and five animal species. SnIV1 genomes assembled from global SRA datasets. Aside from the sequencing efforts from this study presented in Table 2, SnIV1 genomic sequences were assembled from representative SRA datasets. These are from a total of 16 studies, including 15 from palmID search hits, and one recent study identified through literature search (Table  5 ).
In this effort, only a subset of the 80 palmID search hits were used, with the assumption that SnIV1 isolates are possibly not significantly diverse within a single study that analyzed samples collected in the same year and country, by the same research team. The datasets were from a variety of short read Illumina sequencing, with reads of between 47 and 150 nt, single or paired-end, and ranging from 3. 8 to 188. 3 million reads per dataset. SnIV1 genome segments were reconstructed by reference-guided assembly, with highly variable r. m. d. from of as low as 4x to as high as 77,350x. More than 96% of each genome segment was assembled, and only four out of the 63 genome segments (i. e. , representing 21 isolates) were not successfully reconstructed and deposited in GenBank due to partial or fragmented genome assemblies and/or of problems identified in the expected ORFs.
2022; Rivarez et al. 2022 ). The expanded association of solanaceous plants with SnIV1 fits very well with the results of the transmission experiments in this study, where infectivity of SnIV1 was confirmed in several solanaceous hosts. In silico and literature searches implemented here likewise contributed additional information on associated hosts of SnIV1, especially those that were sampled in countries outside of Europe. Most of the plant-derived sequencing datasets with SnIV1 sequences were obtained from metagenomic or metatranscriptomic sequencing of leaf tissues of plants from different families. Interestingly, SnIV1 was detected in the metatranscriptomic sequencing of belowground parts of Fabaceae species (Medicago truncatula and Arachis hypogaea)  (Roux et al. 2014; Chen et al. 2016 ) and in Lithospermum erythrorhizon roots  (Auber et al.
2020). The HTS of L. erythrorhizon has the highest number of SnIV1 reads among all the datasets. In the present study, systemic infection of SnIV1 was likewise demonstrated using RT-PCR assays in different below-and above-ground parts of S. villosum, including roots, whole flowers, individual floral parts, and pollen. SnIV1 sequences were likewise found in a metatranscriptomic sequencing study of Petunia x hybrida (Solanaceae) flowers  (Haselmair-Gosch et al. 2018). Collectively, such diverse host associations gave an unusual impression for a member of the Ilarvirus genus since its members are typically associated with a narrow range of natural hosts within one or a few families  (Badillo-Vargas et al. 2016; Bratsch et al. 2019 ). Moreover, SnIV1 was detected in the metatranscriptomic sequencing of insect samples from the USA and Belgium and, interestingly, in whole honeybees (Apis mellifera)  (Deboutte et al.
The detections of SnIV1 sequences in other sequenced animal tissues such as in Daphnia magna and human blood are suspicious and remain to be resolved, but it is very possible that these represent contaminations that occurred at any step of the viromic experiments. It is important to note that these findings from the viromic surveys and in silico search of SRA datasets do not necessarily imply that the sequenced specimens are hosts of SnlV1. Although mining of SRA datasets for viruses provided useful insights, verification of such sequence mining-derived information using another diagnostic method is difficult, thus, at least the detection of full viral genomes is recommended when analyzing metagenomic data for presence of viruses  (Simmonds et al. 2017; Roux et al. 2019). Aside from true virus infection or symbiotic association with a certain organism, detection of viral sequence in an HTS dataset might result from wet lab contamination, index or barcode hopping, and crosstalk during sequencing  (Lebas et al.