dataesr/scientific-paragraphs-categorization huggingface dataset:
, 1995). Atomic models were refined using Dadimodo  (Evrard et al. , 2011 , Mareuil et al. , 2007 , Roudenko et al. , 2018)  with flexibility introduced in the region corresponding to the hinge between ATPase and Toprim domains (residues 427 to 453). 20 independent calculations were carried out for each system. BLAST searches on the region-loop and the DEEE motif. For the C-loop, BLASTP and TBLASTN searches using a 65-amino acids peptide that includes at its center the 32-amino acids insert as a query were performed against the microbial database. The length of the peptide was chosen in order to specifically hit DNA gyrase sequences. BLAST searches for the C-loop and the.
The final model was used as search model for molecular replacement with the second dataset (holo form, space group P1). Final models all have favorable R / R free values, Molprobity scores and excellent geometry and stereochemistry. The Molprobity overall scores for PDB 6GAV and PDB 6GAU are 1. 74 (100%) and 1. 14 (100%), respectively. Structure determination statistics are provided in Table 1. Figures were prepared using PYMOL.
ACKNOWLEDGEMENTS The authors are grateful to  Ahmed Haouz ,  Patrick Weber  and  Cedric Pissis  for performing robot-driven crystallization trials. They also acknowledge the synchrotron sources ESRF more particularly ID29 and BM29 beamlines (Grenoble, France) and SOLEIL ESRF more particularly SWING and  PX2  ( Saclay, France ) for granting access to their facilities and we thank the staff for constant support and help. They thank  Sebastien Brule  and  Bruno Baron  for performing AUC and CD experiments. They thank  Pierre Roblin  and  Marc Nadal  for helpful discussions. This work was partially supported by grants from  Institut Pasteur ,  Université Paris Diderot and Sorbonne-Université (France).
19/0. 23 0. 19/0. 25 No. atoms Protein 17 437 16 808 Ligand/ion 0 64 Water 47 0 R. m. s. deviations Bonds lengths (Å) 0. 010 0. 010 Bonds angles (°) 1. 13 1. 2 Average B factor 94 100 (Å 2 ) AMP-PNP null 2 Mg 2+ null 2 Ramachandran statistics (%) d Favoured regions 95 88 Allowed regions 4 10 Outliers 0 2 a Values in parentheses are for highest-resolution shell. b The resolution cutoff was selected based on I/σI and CC1/2 according to (Karplus and Diederichs, 2012). c CC1/2 is the percentage of correlation between intensities from random half-datasets. d As defined by MolProbity.
Statistical analysis The data were expressed as mean ± SD of triplicate assays for in vitro assays and mean ± SEM for in vivo assays. Statistical analysis was carried out using the Graph Pad Prism software (one-way analysis of variance ANOVA). IC 50 values were calculated using the OriginPro7. 5 software. The differences were considered significant at *P < 0. 05, **P < 0. 01, and ***P < 0. 001.
Acknowledgments The authors wish to thank the  Ministry of Higher Education and Scientific Research of Algeria  for sponsorship (Grant number:  F00620100006 ). We also appreciate the precious help of  Professor Carlos Gutierrez Merino  from  Universidad de Extremadura  and  Alijandro K. Samhan Arias  from  Universidad Nova de Lisboa, 2829-516 Caparica, Portugal , for their valuable advice.
General methods and materials Moisture sensitive reactions were carried out under argon atmosphere in flame-dried glassware with magnetic stirring bar. Solvents used in the reactions were dried with activated 4Å molecular sieves. Analytical thin layer chromatography (TLC) was purchased from Merck KGaA (silica gel 60 F254). Visualization was accomplished by irradiation with a UV light at 254 nm. Flash column chromatography was carried out using kieselgel 35-70 μm particle sized silica gel (200-400 mesh). Chromatography was performed using silica gel 60 (0. 040-0. 063 mm) from Merck. 1 H NMR and 13 C spectra were recorded with Bruker 500 MHz and 300 MHz instruments. Data for 1 H NMR are reported as follows: chemical shift (δ ppm), multiplicity (app = apparently, s = singlet, d = doublet, t = triplet, q = quartet, sext = sextet, m =multiplet), coupling constant (Hz), integration.
Data for 13 C NMR are reported in terms of chemical shift (δ ppm). The following calibration values have been used for 1 H NMR: CDCl3 (7. 26 ppm), CD3CN, (1. 94 ppm); for 13 C NMR: CDCl3 (77. 0 ppm), CD3CN  (1. 3 and 118. 3 ppm). Mass spectra were obtained from an AEI MS-9 using electron spray ionization (ESI). The HRMS data were measured on a MALDI-TOF instrument for high resolution mass spectra. Infrared spectra were recorded on neat samples, on a Perkin Elmer Spectrum BX FT-IR spectrometer and the characteristic IR absorption frequencies are reported in cm -1. Melting points were recorded using Reichert melting point apparatus and temperatures were uncorrected. All reagents were obtained from commercial supplier and used as received unless otherwise noted. Dienecarbamates were prepared according to literature procedure.
, 2014b). In the risk assessment of PAs, a precautionary approach is taken, in which it is assumed that all PAs have a similar potency as riddelliine  (EFSA CONTAM Panel, 2017). However, this may be too conservative, since, on the basis of available in vitro and in vivo data, the toxic potency of several PAs has been proposed to be lower than that of riddelliine  (Merz and Schrenk, 2016; Chen et al. , 2017). For structurally related chemicals with a common mode of action but with different potencies, a relative potency factor (RPF) approach may be used. For each congener of such a group an RPF is determined by expressing its toxic potency relative to the potency of a reference chemical (or index chemical), usually the most potent congener of the group and/or a congener for which adequate toxicity data for the risk assessment are available.
To obtain more insight in congener-specific differences in toxic potencies of PAs, the present study assessed the concentration-dependent genotoxic responses induced by 37 PAs in human liver HepaRG The necine base can be oxidized at the nitrogen atom, giving rise to PA N-oxides (not shown). cells, using the γH2AX In Cell Western (ICW) assay. This assay detects genotoxic chemicals with diverse DNA damaging properties, including DNA adduct formation, DNA cross-linking and clastogenic effects  (Khoury et al. , 2013). Histone H2AX plays an important role in the DNA damage response and is phosphorylated into γH2AX upon recognition of DNA double strand breaks  (Rogakou et al. , 1998). The in vitro γH2AX induction data obtained in the present study were used to determine the relative potencies of the tested PAs by performing concentration-response modelling using PROAST benchmark dose (BMD) software.
This was followed by a 2-h incubation at room temperature with the primary antibody (Phospho-Histone H2A. X (Ser139) (20E3) Rabbit mAb, Cell Signaling Technology, Leiden, The Netherlands) in PST solution. Subsequently, cells were washed three times with PST solution, and incubated with an anti-goat antibody conjugated to an infrared fluorescent dye (Biotium, Fremont, CA, USA) and RedDot™ 2 (for DNA staining, Biotium) in PST solution. The RedDot™ 2 signal is used as a measure for cell number, allowing normalization of the γH2AX-response to cell number. After 1 h of incubation and subsequent three washes with PST solution, plates were scanned using an Odyssey Infrared Imaging System (LiCor ScienceTec, Les Ulis, France; Application Version 3. 0). Raw data (integrated intensities, I. I (K counts)) were corrected for the background as described before  (Khoury et al.
BMD modelling Differentiated HepaRG cells were exposed in triplicate (n = 3 technical replicates) in 3 independent experiments (n = 3 biological replicates) for 24 h to increasing concentrations of 37 PA congeners, using the genotoxic and carcinogenic mycotoxin aflatoxin B1 as positive control. For each experiment, the mean of each technical triplicate was obtained. For BMD modelling, three datapoints were used for each concentration of the concentration-response dataset (three means from three independent studies). Concentration-response modelling was solely applied on datasets in which at least one of the concentrations gave rise to ≥1. 5-fold γH2AX induction, which has been used as a cut-off for a positive genotoxic response in a previous study in which the γH2AX ICW assay was implemented on HepaRG cells  (Quesnot et al.
, 2016). Concentration-response modelling and benchmark concentration analysis were performed using the PROAST webtool (PROASTweb version 65. 2, RIVM, Bilthoven, Netherlands, https://proastweb. rivm. nl), essentially as recommended by EFSA (EFSA Scientific Committee, 2017). PROAST, as well as the US EPA benchmark dose software (BMDS), is particularly applied for modelling of in vivo (dose-response) data, providing information on the benchmark dose (BMD). We used the PROAST software for the analysis of in vitro (concentration-response) data, thereby providing information on the benchmark concentration (BMC). Tab-delimited text files containing data on concentration, mean γH2AX induction, standard deviation, and sample size (number of biological replicates) were uploaded to the PROAST webtool and analysed as continuous (summary) data.
5-fold. This fourth category includes the three non-esterified necine bases (retronecine, otonecine, heliotridine), the three PA N-oxides (lasiocarpine N-oxide, retrorsine N-oxide and senecionine N-oxide), the platynecine-type PA trachelanthamine, the heliotridine-type (7S) monoesters echinatine and rinderine, and the retronecine-type (7R) monoesters intermedine and indicine. It is of interest to note that the highest concentrations tested of these PAs was 400 μM (except for retrorsine N-oxide, which was tested at concentrations up to 250 μM and trachelanthamine, which was tested at concentrations up to 800 μM). A PA with a BMC 50 of 400 μM would provide an RPF of 0. 0145 (5. 8 μM (= BMC riddelliine)/400 μM). Although these PAs may become active at higher concentrations than 400 μM, it is not likely that the resulting BMC 50 from those concentration-response datasets would be lower than 400 μM, indicating that the PAs that did not cause a 1.
The Innovative Medicines Initiative (IMI) WEB-RADR (Web-Recognising Adverse Drug Reactions) project looked at opportunities and challenges in using social media in pharmacovigilance as a rapidly evolving source of large, real-time data, which could provide new information on the actual use of medicines and potential safety issues. Two of the objectives were to develop principles for continuous monitoring of the safety of medicines without overburdening established pharmacovigilance systems and to propose a regulatory framework on the use of social media in pharmacovigilance. As a starting point, a review of existing legal requirements and regulatory guidance on social media use in pharmacovigilance was performed based on a survey conducted in 2014-2015. Furthermore, input from two large stakeholder workshops and evidence gathered from the research performed by WEB-RADR on the analysis of social media data were taken into consideration.
Key Points Social media can offer opportunities in pharmacovigilance by providing interactive platforms as well as access to large and real-time datasets, although it should be acknowledged that WEB-RADR (Web-Recognising Adverse Drug Reactions) results indicate limited value in detecting signals. A key challenge is obtaining clarity on the regulatory requirements that arise in using social media such as the processing and reporting of safety information. Principles for the use of social media in pharmacovigilance are put forward that take into account existing legal requirements and the need to provide continuous monitoring of the safety of medicines.
This is further acknowledging that these data are derived outside the classic spontaneous reporting systems where the reporting is dependent on initiative and motivation of the reporters  [19] , and that social media data richness may be variable and the follow-up challenging. Taking into account that one important function of spontaneous reporting systems is the early identification of signals  [20] , there are generally concerns that processing social media data into pharmacovigilance databases will reduce the effectiveness of existing signal detection activities due to data dilution. One theory is that data from social media platforms could serve as an early signal from a broader patient population and could supplement conventional pharmacovigilance data sources. Another theory is that social media have limitations due to important differences in the types of information and adverse events described, inherent issues with data reliability  [21]  and the potential risk of diluting important signals originating from current spontaneous reporting systems  [22].
(e) Time period covered by the dataset, including periodicity of updates and the approach to address evolving content (posts/discussions). (f) Social media user demographics where feasible, such as general user profile information (age group, gender), including the representation of specific populations of interest. (g) Taking into account that representativeness of social media data is an area that is still to be further explored, an approach should be defined to mitigate against any 'skewing'  [33]. (h) Generalisability and replicability of the findings of the social media data in the context of the defined purpose should be documented  [38]. (i) Based on the principle of secondary use of data, there should be no planned interactions with the researched community. (j) The adopted data management approach including data curation needs to be defined upfront; this refers to the named entity recognition (NER) problem in the information retrieval community  [31] , which is an active research area.
Social Media as a Data Source for Non-Interventional Studies Social media data may also lend itself as a data source for non-interventional studies. Examples are the evaluation of patterns of the usage of medicines, including the potential for off-label use, lack of efficacy or use of medicines during pregnancy, to measure the effectiveness of risk management measures or to perform health outcome assessments. Curtis et al. [64]  studied the use of data gathered from social media to complement traditional data sources to answer questions regarding comparative effectiveness and safety. To do this, they analysed publicly available social media data including Facebook, blogs and discussion boards for posts mentioning inflammatory arthritis (e. g. rheumatoid, psoriatic). Other examples are the conduct of an observational study on the associations between exposure to and expression of negative opinions about human papillomavirus vaccines on social media  [72]  or a pilot study performed in electronic health records and social media with the objective of better understanding differences and similarities between clinician-reported ADRs and patients' concerns regarding aspirin and atorvastatin  [59].
Acknowledgments: This work has been supported by the  University of Montpellier  (formerly the University of Montpellier-2), the  CNRS (France) , and the  Saint Joseph University of Beirut (USJ). We are grateful to  Dolla Karam Sarkis ,  Vice-Rector  of  USJ , who has been supportive of this work and who worked actively to provide the protected academic time to pursue these researches. We are particularly indebted to  Marianne Abi Fadel ,  Dean of the Faculty of Pharmacy at USJ , for her help and her continuous interest about this work. We want to thank  Maryse Béjaud  for assistance with the NMR instrumentation and for helpful discussions. Finally, we express our gratitude to emeritus  Gérard Lefranc  for all his help and support making the collaboration between our laboratories possible.
Introduction Opting for the classical case-control design with de novo collection of data was traditionally a second choice compared with the cohort design, justified only by efficiency arguments such as the study of rare or very long-term outcomes, i. e. situations where the size or duration of a cohort design seemed impracticable. In addition, the classical case-control methodology being often suspected of being vulnerable to some biases -mainly selection and recall bias-has been also supplanted by the analysis of large electronic healthcare databases (eHCDs) where data on several million patients can be found, collected with no research purpose and thus considered exempted from the said biases; besides, these eHCDs require little effort to get data. However, there are still multiple situations where the de novo, case-based collection of data is necessary.
In observational research, selection bias may result from different causes, some related to the research itself, some related to medical practice, mainly occurring when the identification of cases (outcomes) is related to the exposure or patients' follow-up is differentially related to population-time represented by the reference pool and "controls" for referents selected from that pool and individually matched to cases. exposure. Ad hoc case-control studies are more vulnerable to the first type of bias, but contrary to common belief, selection biases can also occur in cohort studies and even in database studies when physicians are more likely to make certain diagnoses in patients exposed to certain drugs or to follow-up patients because they are prescribed certain drugs. The systematic collection of cases from the systematic case-referent method cannot exclude biases linked to the medical practice itself -but it provides the means to document them as data is clinically collected or with patients themselves.
It is therefore recommended to use sources of information where exposures are recorded prior to the index date. Linking data from the collected clinical database with data from eHCDs  [25] , using patients' medical records obtained from their treating physician  [20]  or hospital medical records where cases were managed after an index disease existing prior to the outcome under study  [14] , or using patients' pharmaceutical file obtained from their pharmacists  [18] , are privileged when possible. In these situations where data must be collected directly from patients (e. g. self-medication, over-the-counter drug use, history of "whole life" treatment, very narrow at risk time window, quality of adherence to treatment, etc. ), it must be done using standardized and thoroughly validated collection tools  [29] [30] [31].
Whenever such a specific factor is not already described in the systematic reference pool, a specific questionnaire can be applied to the casematched controls. Note that this questionnaire needs to not to be necessarily applied to all controls: a two-stage sampling method similar to the one used to document specific risk factors in classical database studies can be applied  [32] , which represents a significant efficiency gain. Finally, when the two above approaches do not provide satisfactory answers to the confounding problem at hand, one must resort to the restricted reference pool approach, where the risk factors of the specific disorder collected can be systematically documented in a restricted pool of reference representing with an index disease, and that would typically include many of the specific, known risk factors for most of the outcomes potentially to be studied.
General reference pool A general reference pool of 15,971 patients representative of the population in contact with the healthcare system was collected in France from a network of 474 general practitioners throughout the country between 2007 and 2014. The population was sampled by sex and age classes so that there were enough patients in each stratum. The representativeness of this sample according to multiple criteria (place of practice, age of doctors, diseases treated and prescribed therapeutic classes) has been verified  [11, 33]. All these patients were documented for their risk factors described in Table  1  and for their drug exposures related to their morbidities or to reasons for visiting the doctor, from 3 sources of information: (i) medical records and prescriptions of physicians; (ii) patient interview; (iii) pharmacist data for a subsample.
Systematic case-referent studies of individual AID in patients of all age groups did not show any association between exposure to all vaccines combined and ITP  [20]  or lupus  [17]. A particularly interesting study done with the same systematic case-referent sample was the surveillance of influenza A / H1N1 vaccine in 2009-2010. An association with an increased risk of GBS was of concern due to the history of the famous "swine flu affair" during a similar vaccination campaign in the USA  [38]. An alert took place a few weeks after launch of the mass A/H1N1 vaccination in Europe, with an epidemic outbreak of GBS reported to health authorities, causing concerns. The systematic case-referent system set up at the time also observed this GBS peak during patients' recruitment (Fig. 2 ), but the analysis performed in real-time, showed that the outbreak was very likely associated with the circulating virus rather than with the vaccination, a finding that other methods and database studies could only confirm later on  [39] [40].
Discussion and new developments The systematic case-referent method has demonstrated its interest and sustainability in many instances and in particular those situations where the in-depth description of the indication and the reasons for prescription, the risk factors and the specificity of the outcome must be precisely and thoroughly documented. This turns out to be actually the case for the study of many risks, and even more so for the study of the effectiveness of drugs in real-life conditions of use. In addition, the method is particularly efficient when real-time monitoring of new drugs is needed, such as when introducing mass vaccination. The method brings answers to most of the criticisms and limitations of ad hoc case-control studies on the one hand, and administrative database studies on the other.
Chiroptical Characterization The LALA-related optical activity of homo PNALALA and of the copolymers was determined by circular dichroism (CD) using a Jasco J810 spectrograph (Lisses, France) equipped with an air cooled 150 W Xenon source. Measurements were performed at 20 • C under the following conditions (C polymer = 0. 025 and 0. 0125 g/dm 3 , 0. 1 mm cell path-length). The CD apparatus was purged with nitrogen to remove oxygen and ozone. The cell temperature was controlled by Peltier effect. Data were expressed in term of measured ellipticity. 4. 2. 5. Formulations of Copolymer/Model Drug Hydrogels Typically, 66 mg of dried copolymer was introduced in a 4 cm 3 flat-bottom test tube and dissolved in 1 cm 3 of phosphate buffer heated at approximately 80 • C using a water-bath. About 30 mg of Risperidone (7.
The samples measured immediately after quenching, as expected, were fully amorphous. The -sharp Bragg's peaks are not visible on their XRD patterns. The amorphous form of EZB/SVT composition stored at standard storage conditions was measured by means of XRD at the beginning once daily for a week, later once a week for a month, and then once a month until 1 year. As can be clearly seen even after 1 year, the examined EZB/SVT system did not reveal any sign of re-crystallization. Comparing this result with the previously published by Knapik et al. , data for pure EZB, one can conclude that SVT impressively stabilizes EZB. It should be emphasized that herein the improvement of physical stability was achieved despite acceleration the EZB's molecular mobility. The similar situation was previously observed only in two cases: Nifedipine + nimodipine and simvastatin + nifedipine  [39, 40].