dataesr/scientific-paragraphs-categorization huggingface dataset:
✓ Language used in the studied texts ✓ ✓ Type of data source, for example, forums or Twitter Keywords the authors used to identify sources or posts of interest ✓ Use of semiautomated processes (mixed methods) ✓ Main results ✓ Whether reported ADRs were highly informative or not ✓ Seriousness of reported ADRs ✓ Reference source was used for comparison with reported ADRs ✓ Identification of potential unexpected ADRs or unexpected frequency of known ADRs ✓ Analysis of the influence of other media, for example, television, radio, or the press, as a potential cause of increased ADR reporting in social media ✓ If the authors mentioned the use of a crawler ✓ Implemented methods of preprocessing ✓ Lay language lexicon or tools used ✓ Authors attempted to identify the relationship between the drug and the event ✓ Authors used a machine-learning approach ✓ Evaluation of the extraction methods with metrics ✓ Comparison with external pharmacovigilance databases ✓ Whether the system enabled evaluating the unexpectedness of any extracted ADRs a ADR: adverse drug reactions.
First, we tested a training set of 20 model chemicals comprised of 10 aneugens, five clastogens and five cytotoxics on three human cell lines (HepG2, LS-174T and ACHN). Our data confirm the robustness of these two biomarkers to discriminate efficiently clastogens, aneugens and misleading cytotoxic chemicals in HepG2 cells. Aneugenic compounds induced either an increase or a decrease in p-H3 depending on their mode of action. Clastogens induced γH2AX, and cytotoxic compounds generated a marked decrease in these two biomarkers. Moreover, the use of different cell lines permits to discriminate direct from bioactivated genotoxins without the need of an 1 3 clastogen mechanism is responsible for MN induction without complementary experiments. Kinetochore and centromere staining assays are available and can be used to characterize the genotoxic mechanism of chemicals  (Hashimoto et al.
After the incubation and three washes, DNA and the biomarker of interest (γH2AX or p-H3) were simultaneously visualized using an Odyssey Infrared Imaging Scanner (LiCor ScienceTec, Les Ulis, France). Raw absorbance data were averaged for the duplicate, corrected for background; the relative fluorescence units from the scanning enabled quantitative analysis. The ICW technique allows cytotoxicity and genotoxicity to be determined in a single experiment  (Graillot et al. 2012a, b; Khoury et al. 2013 ). To determine cytotoxicity, the DNA content (related to the number of cells) recorded in the different treated cells was compared to the DNA content in control cells and was expressed as relative cell count (% RCC). For the determination of genotoxicity, relative fluorescent units for γH2AX (or for p-H3) per cell (as determined by γH2AX (or p-H3) divided by DNA content) were divided by the respective controls (vehicle only) to determine the change in the level of phosphorylation of histone compared with the control.
RESULTS AND DISCUSSION Given that heteroyohimbine biosynthesis likely requires reduction of an iminium present in strictosidine aglycone (Figure  1B ), we used a publically available RNA-seq database that we recently generated  (Gongora-Castillo et al. , 2012)  to search for C. roseus candidates displaying homology to enzyme classes known to reduce the carbonyl functional group. The alcohol dehydrogenases (ADHs), enzymes that reduce aldehydes and ketones to alcohols, were chosen as the initial focus. As part of a screen of ADHs that are upregulated in response to methyl jasmonate  (Gongora-Castillo et al. , 2012) , a hormone known to upregulate alkaloid biosynthesis, we identified a candidate annotated as sinapyl alcohol dehydrogenase (Supplemental Information). When heterologously expressed and purified from E.
EXPERIMENTAL PROCEDURES The THAS gene (accession number KM524258) was cloned into pOPINF and expressed in Rosetta 2 pLysS E. coli cells (Novagen) with induction of expression with 0. 1 mM isopropyl b-D-1-thiogalactopyranoside. Cultures were grown at 18 C for 16 hr, with shaking at 200 rpm. His-tagged THAS was purified using a HisTrap FF 5-ml column (GE Healthcare). SGD expression and purification was done as described for THAS using the expression system described previously by  Yerkes et al. (2008). Purified THAS and SGD were used in all assays. Strictosidine was enzymatically synthesized from tryptamine and a crude methanol extract of snowberries (Symphoricarpos albus) enriched in secologanin prepared as previously described  (Geerlings et al. , 2001). Strictosidine aglycone was generated in situ prior to addition of THAS by incubation of strictosidine and SGD in the appropriate solution for 10 min, at which time strictosidine was completely converted to the aglycone.
Calls regarding drug consumption in an addiction context rose by nearly 0. 2% in 2013. According to the regional health authority (ARS), the prevalence of drug experimentation in the Loire Valley departments in 2010 was approximately 1% of the general population, somewhat lower than the 3% recorded throughout France  (David et al. , 2011). To our knowledge, no epidemiological data on phenethylamine poisonings has been published by any French PCC. The aim of this investigation was to describe the pattern of exposure to all phenethylamines reported to the Angers PCC, as well as the circumstances under which these poisonings occurred and the consequences. The cases were identified through a database search for drugs belonging to the phenethylamine family. Cases between  January, 2007 and December, 2013  were included.
The substances used, demographic characteristics of the patients (age and gender), poisoning context (date, route of consumption), clinical data regarding the intoxications (symptoms, p o i s o n i n g severity and final outcome) and care management details were analyzed. A case was defined as exposure to a drug from the phenethylamine family with or without symptoms, according to the patient's own report or after analytical confirmation. Overall poisoning severity was assessed using the Poison Severity Score  (Persson et al. , 1998)  according to the following grades: none (grade 0), minor (grade 1), moderate (grade 2), severe (grade 3) and fatal (grade 4) poisoning.
This was a retrospective study based on data collected by the Angers PCC during telephone responses to toxicological exposure and during patient follow-up, supplemented by reports from clinical staff in hospital. The Angers PCC manages poisoning cases in western France, which includes around 11 million people and around 30 000 calls per year. The proportion of calls from physicians is 61%, compared to 34% from the general public and 5% from nurses and paramedics. Data were extracted from the Poison Centers database, authorized by the French data protection authority (Commission Nationale Informatique et Liberté, CNIL, accreditation no. 747735). The personal data of patients were anonymized before studying their medical records. Approval of the local Ethics Committee was unnecessary because of the retrospective and non-interventional design of this study.
Crystal data for 3h (major isomer): C 21 H 21 NO 6 , M = 383. 39, orthorhombic, space group P2 1 2 1 2 1 , a = 6. 9024(4), b = 14. 8607 (14), c = 18. 8527(19) Å, V = 1933. 8(3) Å 3 , Z = 4. Refinement of 255 parameters on 2122 independent reflections out of 50852 measured reflections (R int = 0. 031) led to R1 = 0. 044, wR2 = 0. 099, ∆ρ min = -0. 22, ∆ρ max = 0. 15 e Å -3. CCDC-990814 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request/cif.
Acknowledgments The authors thank the  Academy of Scientific Research & Technology (ASRT)  and  National Research Centre, Egypt  and  IMRCP UMR 5623 ,  Faculty of Pharmacy, University Paul Sabatier (France) , for supporting this study within the  IMHOTEP program  (Project no. 109/EGY/FR 8-02 ). In addition, this work was supported by the  France Government  through a fellowship granted by  French Embassy in Egypt  ( Institut Francais d'Egypte ). The authors are grateful to  Professor Dr. Adel Mohamed Bakeer Kholoussy ,  Professor of Pathology, Faculty of Veterinary Medicine, Cairo University , for reading the histopathological sections.
IsoMIF Finder is an online server for the identification of molecular interaction field (MIF) similarities. User defined binding site MIFs can be compared to datasets of pre-calculated MIFs or against a user-defined list of PDB entries. The interface can be used for the prediction of function, identification of potential cross-reactivity or polypharmacological targets and drug repurposing. Detected similarities can be viewed in a browser or within a PyMOL session.
Submitting a job The query MIFs are calculated in cavities of a PDB structure. The user first provides a PDB  (Berman et al. , 2007)  structure by entering a 4 letter-code or uploads its own. The user then chooses to find the top N largest cavities in the PDB or to find the cavity where a specified ligand is bound. Cavities are identified with the Get_Cleft algorithm  (Gaudreault et al. , 2015) , our in-house implementation of Surfnet  (Laskowski, 1995). The user then selects the comparison set, either one of four pre-calculated MIF datasets or a user defined PDB structure list. The four datasets are the human purinome with 2643 MIFs calculated around purine containing ligands bound to human proteins, the scPDB with 8077 MIFs calculated in ligand binding sites  (Desaphy et al. , 2015) , a non-redundant PDB pisces dataset  (Wang and Dunbrack, 2003)  with 14 082 entries and a dataset of 412 entries of structures of drugs bound to their primary targets (or homologue) taken from the PDB website.
Cropping the cavities The cavities identified can sometimes be very large and include nondruggable cavities. By cropping the cavities, the user ensures the query MIFs are calculated in regions of interest. For this, a 3D molecule viewer for each query cavity (Supplementary Fig. S1 ) shows cavity lining residues that can be unchecked, cropping the cavity volume within 3 A ˚radius of their atoms. If the user provided a list of PDB entries for the comparison set, a table lists the cavities found with corresponding information like chains in contact, bound molecules and number of residues in contact (Supplementary Fig. S2 ). Each cavity can be visualized within the protein structure (GIF and PNG) and the user can select only the cavities of interest for the comparison.
Results page A results table for each query cavity lists the top hits found (Supplementary Fig. S3 ) and can be downloaded in CSV format. The top hits can be sorted by Tanimoto similarity or number of nodes (similar probes detected). Additional data provided includes Z-score, the search space (number of grid points sampled), the crystalized ligand around which the hit MIF is calculated (for purinome, scPDB and drug-target datasets only), the PFAM family, Uniprot and protein name. A snapshot of the detected similarities can be visualized in the browser with a GIF or PNG image. Downloadable PyMOL sessions show the query (green) and target hit (cyan) structures with bound molecules and MIF similarities, all superimposed using the transformation matrix that optimally minimizes the pairwise distance between matched probes.
Example application High level of binding site MIF similarity suggests the cavities might share similar molecular functions. In other words, a drug bound in a one could bind the other. Experimental evidence suggests that carbonic anhydrases can be cross-reactivity targets of celecoxib, a COX-2 specific inhibitor and that this approved drug could be repurposed to treat glaucoma  (Weber et al. , 2004). When using the celecoxib binding site of COX-2 (PDB 6COX) as query, carbonic anhydrase XIV (PDB 1RJ6) is found within the top hits of the drug-target dataset (Fig. 1 ; created using the PyMOL session of the MIF similarities). Both ligands have a sulfonamide group surrounded by histidines in both proteins causing the negative charge similarities (magenta spheres). In 6COX, HIS90 could interact with the sulfonamide via hydrogen bonding while in 1RJ6 the interaction would be mediated by a Zn 2þ atom (small grey sphere) coordinated by HIS94 119 and 96.
Identification of existing drugs targeting SARS-CoV-2 human host factors. To identify small molecules targeting human proteins in the SARS-CoV-2 interactome, we sought ligands known to interact with the human proteins, often directly but also by pathway and complexes, drawing on chemoinformatics databases and analyses (Methods). Molecules were prioritized by the statistical significance of the interaction between the human and viral proteins; by their status as approved drugs, investigational new drugs (INDs, "clinical" in Table  1a ,b), or as preclinical candidates; by their apparent selectivity; and by their availability (for purchase availability notes, see Supplemental Tables  3  and 4 ). Chemoinformatics searches yielded 15 approved drugs, four investigational new drugs (clinical), and 18 pre-clinical candidates (Table  1a ), while specialist knowledge revealed 12 approved drugs, 10 investigational new drugs (clinical), and 10 preclinical candidates (Table  1b ).
MATERIALS AND METHODS Genome annotation. The genbank sequence for SARS-CoV-2 isolate 2019-nCoV/USA-WA1/2020, accession MN985325, was downloaded on January 24, 2020. In total, we identified 29 open reading frames and proteolytically mature proteins encoded by SARS-CoV-2  1, 16. Proteolytic products resulting from Nsp3 and Nsp5mediated cleavage of the Orf1a / Orf1ab polyprotein were predicted based on the protease specificity of SARS-CoV proteases  90  , and 16 predicted nonstructural proteins (Nsps) were subsequently cloned (Nsp1-Nsp16). For the proteases Nsp3 (papain-like / Plpro) and Nsp5 (3Clike / 3CLpro), we also designed catalytic dyad/triad mutants: Nsp3 C857A  91   Transfection. For each affinity purification, ten million HEK293T cells were plated per 15-cm dish and transfected with up to 15 μg of individual Strep-tagged expression constructs after 20-24 hours.
0 were added before trypsin digestion. All steps were performed with constant shaking at 1,100 rpm on a ThermoMixer C incubator. Resulting peptides were combined with 50 μl 50 mM Tris-HCl, pH 8. 0 used to rinse beads and acidified with trifluoroacetic acid (0. 5% final, pH < 2. 0). Acidified peptides were desalted for MS analysis using a BioPureSPE Mini 96-Well Plate (20mg PROTO 300 C18; The Nest Group, Inc. ) according to standard protocols. Mass spectrometry data acquisition and analysis. Samples were re-suspended in 4% formic acid, 2% acetonitrile solution, and separated by a reversed-phase gradient over a nanoflow C18 column (Dr. Maisch). Each sample was analyzed on two different mass spectrometers. First, a 75 min acquisition, in which peptides were directly injected via a Easy-nLC 1200 (Thermo) into a Q-Exactive Plus mass spectrometer (Thermo), with all MS1 and MS2 spectra collected in the orbitrap.
For all acquisitions, QCloud was used to control instrument longitudinal performance during the project  94. All proteomic data was searched against the human proteome (uniprot reviewed sequences downloaded February 28th, 2020), EGFP sequence, and the SARS-CoV-2 protein sequences using the default settings for MaxQuant  95, 96. Detected peptides and proteins were filtered to 1% false discovery rate in MaxQuant, and identified proteins were then subjected to protein-protein interaction scoring with both SAINTexpress 20 and MiST  19, 97. We applied a two step filtering strategy to determine the final list of reported interactors which relied on two different scoring stringency cutoffs. In the first step, we chose all protein interactions that possess a MiST score ≥ 0. 7, a SAINTexpress BFDR ≤ 0.
05 and an average spectral count ≥ 2. For all proteins that fulfilled these criteria we extracted information about stable protein complexes they participate in from the CORUM  98  database of known protein complexes. In the second step we then relaxed the stringency and recovered additional interactors that (1) form complexes with interactors determined in filtering step 1 and (2) fulfill the following criteria: MiST score ≥ 0. 6, SAINTexpress BFDR ≤ 0. 05 and average spectral counts ≥ 2. Proteins that fulfilled filtering criteria in either step 1 or step 2 were considered to be HC-PPIs and visualized with cytoscape  99. Using this filtering criteria, nearly all of our baits recovered a number of HC-PPIs in close alignment with previous datasets reporting an average of ~6 PPIs per bait 100.
However, for a subset of baits (Orf8, Nsp8, Nsp13, and orf9c) we observed a much higher number of PPIs passing these filtering criteria. For these four baits, the MiST scoring was instead performed using an larger in-house database of 87 baits that were prepared and processed in an analogous manner to this SARS-CoV-2 dataset. This was done to provide a more comprehensive collection of baits for comparison, to minimize the classification of non-specifically binding background proteins as HC-PPIs. All mass spectrometry raw data and search results files have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD018117  101, 102. PPI networks have also been uploaded to NDEx. Gene Ontology Over-representation Analysis. The targets of each bait were tested for enrichment of Gene Ontology (GO Biological Process) terms.
The over-representation analysis (ORA) was performed using the enricher function of clusterProfiler package in R with default parameters. The gene ontology terms were obtained from the c5 category of Molecular Signature Database (MSigDBv6. 1). Significant GO terms (1% FDR) were identified and further refined to select non-redundant terms. In order to select non-redundant gene sets, we first constructed a GO term tree based on distances (1-Jaccard Similarity Coefficients of shared genes) between the significant terms. The GO term tree was cut at a specific level (h=0. 99) to identify clusters of non-redundant gene sets. For a bait with multiple significant terms belonging to the same cluster, we selected the broadest term i. e. largest gene set size. Virus Interactome Similarity Analysis. Interactome similarity was assessed by comparing the number of shared human interacting proteins between pathogen pairs, using a hypergeometric test to calculate significance.
The background gene set for the test consisted of all unique proteins detected by mass spectrometry across all pathogens (N=10,181 genes). Orf6 peptide modeling. The proposed interaction between Orf6 and the NUP98-RAE1 complex was modeled in PyRosetta 4 103 (release v2020. 02-dev61090) using the crystal structure of Vesicular stomatitis virus matrix (M) protein bound to NUP98-RAE1 as a template 66 (PDB 4OWR downloaded from the PDB-REDO server  104  ). The M protein chain (C) was truncated after residue 54 to restrict the model to the putative interaction motif in Orf6 (M protein residues 49-54, sequence DEMDTH). These residues were mutated to the Orf6 sequence, QPMEID, using the mutate_residue function in the module pyrosetta. toolbox, without repacking at this initial step. After all six residues were mutated, the full model was relaxed to a low energy conformation using the FastRelax protocol in the module pyrosetta.
rosetta. protocols. relax. FastRelax was run with constraints to starting coordinates and scored with the ref2015 score function. The resulting model was inspected for any large energetic penalties associated with the modeled peptide residues or those NUP98 and RAE1 residues interacting with the peptide, and was found to have none. The model was visualized in PyMOL (The PyMOL Molecular Graphics System, Version 2. 3. 4 Schrödinger, LLC. ). CUL2 ZYG11B homology model generation. The CRL2 ZYG11B homology model was built with Swissmodel 105 and Modeller 106 by using the homology template of each domain from PDB database (PDB codes: 4b8o, 5jh5,1g03, and 6r7n). The ZYG11B model has two structured domains: a leucine rich repeat (LRR) and Armadillo Repeat (ARM) at the N and C-terminus respectively.
Alignment of Protein E and Histone H2A. In order to align protein E and histone H2A, the structure of the protein E SARS-CoV homolog (PDB ID: 2MM4) was compared to the human nucleosome structure (PDB ID: 6K1K). Protein E was structurally aligned to the histone subunits using Pymol's "align" function (https://pymolwiki. org/index. php/Align). Align performs a sequence alignment followed by a structural superposition, and then carries out zero or more cycles of refinement in order to reject structural outliers found during the fit. The best superposition was obtained for H2A residues 49-60 & 63-70 and Protein E residues 25-44 at an RMSD of 2. 8Å, as reported in Figure  4d.
Chemoinformatic Analysis of SARS-CoV2 Interacting Partners. To identify drugs and reagents that modulate the 332 host factors interacting with SARS-CoV-2-HEK293T (MiST >= 0. 70), we used two approaches: 1) a chemoinformatic analysis of open-source chemical databases and 2) a target-and pathwayspecific literature search, drawing on specialist knowledge within our group. Chemoinformatically, we retrieved 2,472 molecules from the IUPHAR/BPS Guide to Pharmacology (2020-3-12) that interacted with 30 human "prey" proteins (38 approved, 71 in clinical trials), and found 10,883 molecules (95 approved, 369 in clinical trials) from the ChEMBL25 database 107 (Supplementary Tables  5, 6 ). For both approaches, molecules were prioritized on their FDA approval status, activity at the target of interest better than 1 μM, and commercial availability, drawing on the ZINC database  108.
FDA approved molecules were prioritized except when clinical candidates or preclinical research molecules had substantially better selectivity or potency on-target. In some cases, we considered molecules with indirect mechanisms of action on the general pathway of interest based solely on literature evidence (e. g. , captopril modulates ACE2 indirectly via its direct interaction with Angiotensin Converting Enzyme, ACE). Finally, we predicted 6 additional molecules (2 approved, 1 in clinical trials) for proteins with MIST scores between 0. 7-0. 6 to viral baits (Supplemental Tables  3  and 4 ). Complete methods can be found here (www. github. com/momeara/BioChemPantry/vignette/COVID19). * (i) (iii) (ii) (i) (ii) (i) (ii) (i) (ii) (iii) (iv) (iii) (iv) G3BP1 G3BP2 FAM98A CSNK2B SNIP1 N CSNK2A2 LARP1 MOV10 PABPC1 RRP9 RPL36 PABPC4 RBM28 UPF1 DDX21 RNA Processing Stress Granules Zotatifin FKBP7.
Colors indicate chemical properties of amino acids: polar (G,S,T,Y,C, green), neutral (Q,N, purple), basic (K, R, H, blue), acidic (D, E, red),and hydrophobic (A, V, L, I, P, W, F, M, black). (iii) SARS-CoV-2 Orf6 carboxy-terminal peptide modeled into the binding site of the VSV M protein-NUP98-RAE1 complex (PDB ID: 4OWR). Orf6 shown in dark purple, M protein in yellow, NUP98 in green, and RAE1 in light purple. Orf6 and M protein residues labeled. RAE1 hydrophobic residues contacting the key methionine and basic patch residues of RAE1 and NUP98 are shown. (iv) Putative NUP98-RAE1 interaction motifs present in proteins from several viral species. The consensus motif consists of negatively charged residues (red) surrounding a conserved methionine (yellow). (c) Orf10 interacts with the CUL2 ZYG11B complex.
and Nsp5 C145A 92,93. Open reading frames at the 3' end of the viral genome annotated in the original genbank file included 4 Structural proteins: S, E, M, N, and the additional open reading frames Orf3a, Orf6, Orf7a, Orf8, and Orf10. Based on analysis of open reading frames in the genome and comparisons with other annotated SARS-CoV open reading frames, we annotated a further four open reading frames: Orf3b, Orf7b, Orf9b, and Orf9c. Cell culture. HEK293T cells were cultured in Dulbecco's Modified Eagle's Medium (Corning) supplemented with 10% Fetal Bovine Serum (Gibco, Life Technologies) and 1% Penicillin-Streptomycin (Corning) and maintained at 37°C in a humidified atmosphere of 5% CO2.