dataesr/scientific-paragraphs-categorization huggingface dataset:
Exercise paradigm The hardware and software materials used in this study were previously described. 38, 39 Briefly, the running wheels, made of polycarbonate disc which allows an open running surface, were mounted on a plastic base and inclined at a 35°angle (ENV-044, Med Associates; St Albans, VT, USA). The base was fixed on a stable transparent acryl-glass plate. The free-wheel-running activity was recorded permanently during the entire pre-testing period (42 days). The data of each wheel were transferred in real time to a USB interface hub (DIG-804, Med Associates) via a wireless system which relayed data to a Wheel Manager Software (SOF-860, Med Associates). Descriptive statistics of wheel-running activity are displayed in Fig. 1.
Behavioural test chambers Mice locomotor activity was measured using a pool of eight chambers (one mouse being tested per chamber) and the data were collected through the connection to a customwritten software. These activity chambers were made of a removable transparent polycarbonate tub (22 × 12 cm surface × 12 cm height), fixed on a stable base constituted of a black-paint wooden plank. Infrared light-beams, located on the two long sides of the tub (at 2 cm heights from the floor, 8 cm apart and spaced 6. 5 cm from each end of the tub), emanated from two photocell sources and detectors which were mounted on the plank. The locomotor activity was measured in terms of crossings, one crossing being counted every time a mouse broke successively the two parallel beams. During the testing, sound-attenuated shells artificially ventilated and illuminated by a white light bulb, individually encased the activity chambers.
The list-mode emission data were histogrammed into 3D sinograms by Fourier rebinning and reconstructed by filtered backprojection with a ramp filter cut-off at the Nyquist frequency. All corrections were applied except for scatter events. 47 o partial volume correction was performed on the acquired data. A set of 3D images was reconstructed in a 256 × 256 × 95 matrix and a zoom factor of 2. The reconstructed voxel size was 0. 4 mm × 0. 4 mm × 0. 8 mm. The dynamic time framing was set as follows: 6 × 5, 6 × 10, 3 × 20, 5 × 30, 5 × 60, 8 × 150, 6 × 300 s and all data were decay corrected to the beginning of each individual frame. Immediately after PET acquisition, the anaesthetized mice were transferred into a 9. 4 T MRI DirectDrive VNMRS horizontal bore system with a shielded gradient system (Agilent Technologies, Palo Alto, CA, USA).
A 72 mm inner diameter volumetric coil and a two-channel head surface coil (Rapid Biomedical GmbH, Würzurg, Germany) were used as transmitter and receiver coils, respectively. The 3D anatomical T 2 -weighted brain images were acquired with a fast spin-echo multislice sequence using the following parameters: TR/TEeff = 2500/40 ms, matrix = 128 × 128 × 64, FOV = 20 mm × 20 mm × 10. 5 mm, voxel size: 0. 156 mm × 0. 156 mm × 0. 164 mm and a total acquisition time of 21 min. Imaging data were processed with PMOD software (version 3. 7, PMOD Technologies Ltd, Zurich, Switzerland). The processing includes a manual rigid co-registration of individual MRI images to its corresponding PET images, a spatial normalization of the co-registered MRI onto the PMOD MRI template and the extraction of the PET time activity curves of the left and right striatum as well as the cerebellum.
Briefly, the inverse deformation parameters obtained during the spatial normalization of the individual MRI images onto the PMOD template were used to bring the mouse brain atlas into the native dynamic PET space and then extract the time activity curves (TACs) based on the atlas predefined structures. 48, 49 The extracted TACs were then transferred into the kinetic modelling module of PMOD in order to estimate the [ 18 F]fallypride binding. The non-displaceable binding potential (BP ND ) parameter was calculated using the multi-linear reference tissue model 2 with the cerebellum as reference tissue. 50, 51 We controlled the homogeneity of the TACs in the reference region (i. e. cerebellum) between each group, to rule out any bias from radiotracer inputs variations in the reference region.
Total RNA 1 Results below the limit of quantification or limit of detection = BLQ. Uridine 5 -diphospho-glucuronosyltransferase = UGT. Empty cells indicate no data. 2 The small intestine-to-liver ratio is the ratio of the estimated total small intestine abundance to the total hepatic abundance using the data from the same study for both tissues. These are calculated assuming total microsomal (or S9) protein abundance in the small intestine is 4% of total microsomal (or S9) protein abundance in the liver. Total RNA samples are assumed to represent the UGT levels in intestinal mucosa; intestine to liver ratio is calculated assuming intestinal mucosa mass is 8% of liver mass. 3 UGT1A7, UGT1A8, and UGT1A10 are considered to be extrahepatic enzymes  [29]. However, low hepatic UGT1A7 expression has been reported  [10]  and has been used for parameterizing the current GastroPlus physiology model.
In Silico Example: Predicted Substrate Specificity for UGT Enzymes Knowing that a compound is subject to metabolism by Phase I, Phase II, or hydrolytic cleavage is key to understanding its bioavailability and, if it is a prodrug, its bioactivation. Prediction of which metabolites are most likely produced by these metabolic mechanisms is also critical to understanding the toxicology, efficacy, and pharmacokinetic properties of drug candidates. All the UGT substrates included in the examples in this publication have aliphatic or aromatic hydroxyl groups and do not require phase I metabolism prior to conjugation. The Metabolism Module of ADMET Predictor ® (Simulations Plus, Inc. , Lancaster, CA, USA) provides substrate classification models for nine human UGT isoforms. Much more data are available for CYP enzymes, and correspondingly, more detailed models are available for CYPs, including models for estimating kinetic parameters as well as ones for substrate and inhibitor classification.
Acknowledgments: The authors thank  Bob Clark  for helping describe to the development of the UGT substrate classification models. We also thank  Kevin Litwiler  for his review and helpful comments. Conflicts of Interest: M. B. R. worked for  Array BioPharma  at the time she worked on modeling for raltegravir and atazanavir, and she currently works for  Pfizer. G. F. , J. S. M. , J. M. M. , M. B. B. , N. M. and V. L. work for Simulations Plus, the software company of GastroPlus. Journal fees were also paid by  Simulations Plus. The manuscript reflects the views of the scientists, and not the company.
In the explanatory family, Lee et al. [13]  associate SIDER side effects and GO biological processes through drugs, by the combination of a Drug-Side effect and a Drug-Biological process networks. Highlighted relationships are obtained using statistical approaches (i. e. , enrichment and t-score) and evaluated in regard to cooccurrences in PubMed abstracts. Wallach et al. link drugs to proteins through molecular docking, then to pathways through the KEGG database, and use logistic regression and feature selection approaches to select pathways most probably impacted in side effects  [14]. Bresso et al. [15]  group frequently associated drug reactions and propose elements of explanations of their grouping using Inductive Logic Programming. Also, Chen et al. [16]  propose a computational algorithm to infer Protein-ADR relationships from a network of protein-protein interactions, ADR-ADR similarities and known protein-ADR relations.
In the same vein, KGPTree  [25]  extracts paths of the form root → predicate → entity → predicate. → entity, while allowing generalizations on both predicates and entities, whereas we offer generalization on entities only. However, they only allow a generalization to a unique and broad type denoted with a wild card (*), while we allow a granular generalization following ontology class hierarchies. FeGeLOD and KGPTree extract only paths and path patterns, whereas one may want to extract other common structures such as subtrees. Mustard Python library offers such functionalities applying Graph Kernels on RDF graphs, plus additional facilities such as detecting hubs or low frequency patterns  [26, 27]. However, it does not allow generalization operations. The contribution of our work is twofold: first, we show that knowledge graphs provide sufficiently diverse features to enable simple and explainable models to distinguish between drugs that are causative or not, for two types of ADRs commonly monitored; second, we manually evaluate in a mini-study that in this setting, predictive features constitute good candidates for investigating ADR mechanisms further.
---------------------→ geneatlas_vocabulary: Resource is turned in interactsWith -------→ Enzyme ). Because of the limited number of features in rules, this translation is made manually, on the basis of descriptions of predicates, classes, and entities found in their original knowledge graph or database. We asked three experts in pharmacy and pharmacology to review independently each attribute (i. e. , feature) of the rules, to evaluate if they may be explanatory for ADRs. Each expert has to answer a voluntarily simple three-way question: "according to your own knowledge or the state of the art, do you think that the feature is explanatory for DILI?" (SCAR, respectively). Possible answers are "yes", "maybe (possible, but not obvious)" and "no (probably not explanatory)". We allow expert to check the literature or any state-of-the-art resource, but up to 15 min, since we consider that more time causes to fall in the "no (probably not explanatory)" option.
To guide their decision on each feature, experts are provided with two Web links: one pointing to the list of drugs from the ⊕ train set that supports the feature; one to the page of the main entity mentioned in the feature (i. e. , the ∀a∈A a ∧ ∀b∈B ¬b ⇒ c neighbor node, or the final node of the path or path pattern) in an expert database: DrugBank, ChEBI, KEGG, QuickGO, or MeSH browser, depending on the namespace of the node. After expert reviews, answers were normalized, under their supervision, to guarantee all experts interpret the negation of features the same way. For each feature we check if at least one, two or three of the experts think it is or may be explanatory, if the three think it is explanatory and if the three think it is not. In addition, we compute Cohen's kappa coefficient to evaluate the average agreement between experts with two different settings: considering the three different answers as distinct, or considering answers "yes" and "maybe" as a unique positive answer.
Expert evaluation JRip produced 6 and 5 rules for DILI and SCAR, respectively. The translation of these rules is available in Additional file 1: Tables  S1  and S2. After removing uninformative features, we obtained 11 and 13 distinct features, respectively. Those are provided in Additional file 1: Tables  S3  and S4. These features are those reviewed by our three experts in pharmacology (CB, CNC, and NP). Quantitative performances of the JRip algorithm are presented in Table  6  for comparison with CART. On both datasets (DILI and SCAR) differences in performances (Precision, Recall, Accuracy, F-measure) are not statistically significant (t test, p < 0. 05). The ratio (and number) of features for which experts reached an agreement, or for which at least one, two or three of the experts think they are or may be explanatory (answers "yes" or "maybe") are provided in Table  8.
The capillary voltage was set at 2. 3 kV, and desolvation gas temperature and flow were maintained at 350 °C and 650 L/h, respectively. Identification and quantification of each analyte were carried on according to the mass-to-charge ratio (m/z) of the pseudo-molecular ion of each compound ([M + H] + ). Cone voltage optimal setting was 35 V and the pseudo-molecular parent ion corresponding to SPL/CAN, TMSPL and MeT have an m/z of 341. 0, 389. 0 and 303. 0 respectively. SPL was detected with m/z of 341. 0, which corresponds to the pseudo-molecular parent ion of CAN. This is due to the cleavage of the 7αthioacetyl group of SPL during the electrospray evaporation and ionization process within the ESI source producing CAN. Dwell time was set at 5 ms for all the compounds. Data processing was performed using Waters MassLynx software version 4.
Viscosity measurement Viscosity was measured using the Haake ® Mars ® 40 rheometer fitted with a cone-plate measuring geometry (sensor C35/2° Ti -35 mm diameter, cone-plate angle of 2°). Rheological measurements and data processing were performed with the RheoWin software. 400 µL hydrogel was deposited in the middle of the plate then the measurement was set in the rotary mode, with a shear rate of 4. 3 s -1 over 30 s at 20 °C.
Acknowledgements ND and YNK acknowledge financial support from the  Swiss Commission for Technology and Innovation  ( CTI  Project  19086. 1 PFLS-LS ) and  Apidel SA (Geneva, Switzerland). YNK would like to thank the  University of Geneva , the  Fondation Ernst and Lucie Schmidheiny  and the  Société Académique de Genève  for providing financial support to enable the acquisition of the Waters Xevo ® TQ-MS detector. We would also like to thank  Dr Christoph Bauer  and  Jérôme Bosset  from the  Bioimaging Center of the University of Geneva  for their precious advice with TEM and CLSM.
Methods The Pharmacovigilance network consists of a ring of 31 regional pharmacovigilance centers (RCPV) dotted around the French territory. The French Pharmacovigilance database (FPDB) centralizes all spontaneous reports of ADRs, in order to accrue the European database EudraVigilance. Such ADR reporting is mandatory for every health professional and respects the anonymity of both patients and notifiers. As the Nice RCPV has drug-induced Long QT syndrome and cardiac safety of drugs within its scope of expertise, in March 2020, the National drug Agency (ANSM) appointed us to investigate all cases of cardiotoxicity associated with the empirical use of LPV/r against COVID-19 that had spontaneously been reported by any of the 31 RCPVs. After a first expertise by the RCPV of origin, all cases of cardiotoxicity were consecutively included in this study.
Cases were analyzed, validated and a score of imputability was associated with each of them  [21, 22]  for causality assessment. Cases were not included if chronological and semiological data ruled out the role of the designated drug in the notified effect (i. e. the effect was preexisting before the drug administration). To assess the completeness of the case-series we performed an extraction of the FPDB back-dated to March 1st, 2020, involved a research by treatment indication "coronavirus infection" or "COVID-19" and by drugs. Cases related to another indication than COVID-19 were excluded. Each cardiac ADR transmitted was reviewed by a pharmacovigilant first, to assess missing data required for expertise, and in particular the electrocardiograms (ECGs) before, during and after treatment discontinuation, when necessary.
Symptoms, patient characteristics (sex, age, comorbidities), therapeutic regimens and prescription dates, time-to-onset, outcome of ADR, drug concentrations when available, concomitant drugs (especially those known to induce QT prolongation), kalemia, magnesemia, renal function and other reported possible risk factors were gathered. Missing data were requested of the initial health professional reporter through the RCPV of origin. When available, lopinavir residual plasma concentration were averaged and compared with normal values  [23]. Two residents and a cardiologist trained in the field measured all available ECGs with classical methods  [24]  and the help of a digital caliper (Iconico®, CardioCalipers®). QT intervals were calculated on 3 consecutive complexes, in lead D2 if possible (most of the times), and corrected according to the Bazett formula (QTcB).
A scrupulous screening was then performed to identify each cardiac adverse effect. Descriptive statistics are expressed as mean ± standard deviation with minimal and maximal values according to the model mean ± SD [min;max]. Percentages were calculated for qualitative data. For kalemia and QTc the difference between the value at the time of the ADR and the value at LPV/r instauration was calculated under the description "delta". Normality of data was assessed with the Shapiro-Wilk test. QTc mean comparisons were made using paired Student "t" two-sided test with results given as point estimates and 95% confidence intervals (CI95). Chi 2 test was performed to evaluate the association between high residual loponavir values and severity of the ADR. P values lower than 0. 05 were considered as statistically significant.
Subjects and Sampling Procedure The subjects were cocaine users, recruited between 2012 and 2016 in nine urban out-or inpatient specialized treatment centers were they were currently treated for substance use disorders in France (NCT01569347). They were recruited if they used cocaine regardless of whether they met criteria for cocaine use disorder. As European ethnicity was not an inclusion criterion, we a posteriori selected subjects with European ancestry by comparing identity by descent to the 1000Genome database, as described in Marees et al. (33).
Subjects' Characteristics The original sample consisted of 418 subjects. Clinical data on lifetime CACP history were available for 316 subjects; among them, 290 were identified as having European ancestry. Among the 316 subjects, 68 were women (21. 5%) and 248 were men (78. 5%). The mean age was 37. 5 years (SD ± 8. 7). The median length of regular cocaine use was 5 years. The frequency of cocaine use during the worst period was daily for 60. 1% of the subjects and weekly for 29. 4%. Crack use was described by 27. 8% of the subjects, and smoked or/and intravenous cocaine use was preferred by 38. 3%. Lifetime alcohol and cannabis dependence were observed in 63. 2 and 61. 2% of the subjects, respectively. Among the 140 subjects (44. 4%) with lifetime OMT prescription, 84 were currently on methadone.
FUNDING This study was funded by the  French Ministry of Health  ( Programme Hospitalier de Recherche Clinique PHRC National 2010   AOM10165 ) to FV. The  Délégation de la Recherche Clinique et du Développement (DRCD)  from the  Assistance Publique-Hôpitaux de Paris (APHP)  endorsed the study. Furthermore, FV received in the past 5 years grants from the  ERANET-Neuron Joint Transnational Call for European Research Projects on Synaptic dysfunction 2017  ( ANR-17-NEU3-0002-05 ), from the  Labex BioPsy  (Twice in 2016), a  French state  fund managed by the  ANR  within the  Investissements d'Avenir program  under reference  ANR-11-IDEX-0004-02 , from the  Fondation pour la Recherche en Alcoologie (Fondation de France ) 2016 and 2018, from the  French Ministry of Health  ( Programme Hospitalier de Recherche Clinique PHRC National   2018 PHRC-N-180777 ), and from the.
The uptake process was rapid: in one hour, around a third of the NPs were internalized. In addition, the internalized NPs were visualized by confocal microscopy. The fluorescent cellular stacks were analyzed using a freely available macro for ImageJ software, Particle_In_Cell-3D. The internalized objects were localized and counted. This methodology could serve for further studies while analyzing the effects of NPs size, shape and surface properties on their interaction with various cell lines.
However, we intended here to go steps beyond by quantifying the number of internalized NPs in each cell. To do so, the free ImageJ macro Particle_In_Cell-3D was updated and used here (https://imagejdocu. tudor. lu/macro/particle_in_cell-3d) (Torrano et al. , 2013). Briefly, the program analyzes images stacks where both cell membranes and NPs have to be fluorescently marked in order to create an accurate 3D representation of the cell with the objects inside, and to quantify the said objects. In Fig. 5. C is displayed the mask that the macro creates before producing the 3D representation with the help of the ImageJ plugin 3D Viewer (Fig. 5. D). The virtual cell can then be rotated and cut open in order to observe the internalized NPs (Fig. 5. E and F). Thus, when a sufficient number of cells is analyzed, Particle_In_Cell-3D could serve as a statistical tool to quantify, for each time point in a kinetic study, the mean number of NPs internalized by immune cells.
Acknowledgements This project has received financial support from the  CNRS  through the  80 Prime program. We acknowledge support from the  French Agence Nationale de la Recherche (ANR)  under the grant " SoftGlue " ( ANR-20-CE19-0020 ) and from the  Labex NanoSaclay  ( ANR-10-LABX-0035 ). We thank the platform  CPBM/CLUPS/LUMAT FR2764. The present work has benefited from the  Imagerie-Gif core facility , supported by  ANR  ( ANR-11-EQPX-0029 /Morphoscope ,  ANR-10-INBS-04  /  FranceBioImaging ;  ANR-11-IDEX-0003-02/Saclay Plant Sciences ).
Gas Chromatography-Mass Spectrometry This procedure was performed with a Finnigan PolarisQ GC/MS ion trap Bench-top mass spectrometer (Thermo Electron Corporation, Walthman, MA, USA). All conditions were as above, except that the carrier gas was He at a linear velocity of 31. 9 cm. s -1 and DB-5MS (30 m × 0. 25 mm × 0. 25 µm) capillary columns were used. The positive ion electron ionization mode was applied, with a mass range of 40-400 amu. Identification of compounds was based on comparisons with published MS data  [18]  and a computer library search (the database was delivered together with the instrument) and by comparison of their Kovats indices with those of authentic references and with literature values  [18]. The identification was confirmed with the aid of authentic samples. Kovats indices were calculated mainly from the GC-MS analysis results  [19].
Acknowledgments: In memory of  Luis Beltran Rojas Fermin  ( 1963-2020 ), Director of the  Research Institute, Faculty of Pharmacy and Bioanalysis, University of Los Andes, Mérida, Venezuela , for his outstanding career in Natural Products research where he achieved more than 160 published papers in Phytochemistry, Essential oil characterization, and diverse biological activity assays. On behalf of his colleagues and friends, we would like to express our gratitude for all the knowledge and valuable shared moments.
Ethnobotanical Data Collection From April 2017 to June 2019, a fieldwork survey was carried out in order to lay down a thorough ethnobotanical study of poisonous and potential poisonous species with significant toxicity effects. Ethnobotanical data were collected from 386 informants aged 20 and up from various districts in northeastern Morocco (Table  1 ). The study area included the Oujda-Angad, Berkane, and Jerada districts. These areas were selected because of their cultural and botanical diversities. The sampling technique used in this ethnobotanical investigation was simple random sampling. The data were obtained through structured and semi-structured interviews and face-to-face conversations with local inhabitants and herbalists of the study areas. Permission were obtained from the interviewees prior to the interview.
Plant Collection and Identification The names of the plant species mentioned by the interviewees were identified using technical documentation related to Moroccan flora  [34] [35] [36]  and compared with the specimens already in the Herbarium of Mohammed First University Oujda-Morocco (HUMPOM). A voucher number was assigned to each specimen and deposited in the HUMPOM. The scientific names of plants selected were checked against The Plant List Database  [37].