dataesr/scientific-paragraphs-categorization huggingface dataset:
1 This work was supported by the  European Commission Seventh Framework Programme for Research and Technological Development Framework  (grant no. KBBE-2007-3-1-01  to the  SMARTCELL  project), the  Centre National de la Recherche Scientifique  and the  Région Alsace (Bourse de doctorat pour ingénieur  to J. -F. G. ), the  European Fund for Regional Development in the INTERREG IVA Broad Region EU Invests in Your Future programme  (to B. B. ), the  Agence Nationale de la Recherche  (grant no. ANR-10-BLAN-1528  to H. R. and  D. W. -R. for the  PHENOWALL  project), and the  University of Strasbourg Institute for Advanced Study  and the  Freiburg Institute for Advanced Studies  (funding for the  METABEVO  project).
Plumbagin Induces Different Levels of Intracellular ROS in U937 vs. Raji Cells Published data demonstrated the capacity of plumbagin to elicit ROS in cancer cells  [26, 27, 49]. Analysis of intracellular ROS production in U937 and Raji cells exposed to plumbagin was performed by flow cytometry analysis after staining with 2',7'-dichlorodihydrofluorescein diacetate (H 2 DCFDA). As shown in Figure  3A, B , plumbagin induces ROS production in both cell lines tested. U937 cells show a very robust increase in intracellular ROS already after 15 min treatment at 1 μM plumbagin. Raji cells, in contrast, show a much milder intracellular ROS increase compared to the positive control hydrogen peroxide (H 2 O 2 , 50 μM). The level of ROS production induced by plumbagin is ten-fold higher in U937 compared to Raji cells.
Untreated cells did not show any modulation of fluorescence over the time as expected (data not shown). This aspect may reveal specific aspects related to the intracellular metabolism of the compound and may deserve future investigations, beyond the scope of this study. Altogether, we can exclude a differential internalization or efflux of the compound as the responsible factor for lower ROS levels in Raji. Samples were collected and analyzed by flow cytometry without additional staining; (B) Plumbagin efflux assay was performed after 30 min of incubation with the same concentration of plumbagin used for the uptake assay, followed by recovery in plumbagin-free medium (see "Experimental" section)  [50]. U937 and Raji cells were collected at indicated times and their auto-fluorescence was analyzed by flow cytometry.
Acknowledgments François Gaascht was supported by a fellowship from the  European Union  ( ITN "RedCat" 215009, Interreg IVa project  " Corena "). Marie-Hélène Teiten  is supported by  Télévie grants  ( Fonds National de la Recherche Scientifique, Belgium ). Claudia Cerella  is supported by a "Waxweiler grant for cancer prevention research" from the  Action Lions "Vaincre le Cancer ". Research at LBMCC is financially supported by the  Fondation de Recherche Cancer et Sang , the  Recherches Scientifiques Luxembourg association , the  Een Haerz  fir kriibskrank Kanner association, the  Action Lions Vaincre le Cancer association , the  European Union  ( ITN "RedCat" 215009, Interreg IVa project  " Corena ") and the  Télévie Luxembourg. Mario Dicato  is supported by the  National Research Foundation (NRF)  by the  MEST of Korea for Tumor Microenvironment Global Core Research Center (GCRC) grant  [grant No.
Statistical analysis All the experiments were repeated thrice to validate the reproducibility. The results were expressed as geometric means and were reported with 95% confidence intervals. For statistical analysis (Chi squared test), SPSS 10 and Epi-Info 6. 04 (CDC, Atlanta, GA, USA) were used. Probit analysis was used to calculate the CQ EC 50 for each isolate  [22, 26]. Linkage disequilibrium constants (D' and r 2 ) were also calculated  [27] [28].
Statistical analysis Multivariate data analysis was performed with the SIMCA 13 software (Umetrics, Sweden) to predict clinical status based on protein signatures in 1) BUD patients before antibiotic treatment (BUD 0) versus healthy controls, 2) BUD patients 4 weeks after the end of the 8 week antibiotic treatment (BUD 12) versus healthy controls, 3) BUD 0 versus BUD 12 and 4) fast versus slow healers. We used conventional Principal Component Analysis (PCA) to gain a first insight into the general distribution of our dataset and detect outliers. In complement, Orthogonal Partial Least Squares-Discriminant Analysis (O-PLS-DA) was performed to identify the variables contributing to group classification  [22]. The quality of each model was assessed by calculation of Fit (R 2 ) and prediction ability (Q 2 ) values.
To further characterize univariate differences between controls, BUD 0 and BUD 12 patients, the nonparametric Wilcoxon ranksum (Mann-Whitney) test was used. Paired samples for BUD patients at week 0 and week 12 were compared using the Wilcoxon signed rank test. As this led to performing 71 tests for each comparison, we adjusted for multiple testing by defining as significant all p-values,0. 05 that also guaranteed a false discovery rate (FDR) under 1%  [23]. Univariate analysis and multiple testing adjustments were performed using R software (version 2. 13. 2, R Development Core Team, R Foundation for Statistical Computing, Vienna, Austria) and package QVALUE v1. 0  [24]. The GraphPad Prism software (v5. 0d, La Jolla, CA) was used for box-andwhisker plot representation, with outlier cut-off determined by Tukey's test.
Differential inflammatory and metabolic profiles in faster healing patients To identify biological correlates of healing, we then subjected our data to a multivariate analysis comparing serum protein levels measured at the time of inclusion in antibiotic therapy in ''fast healers'' (FH) and ''slow healers'' (SH). For this purpose, a cut-off healing time of 12 weeks was chosen, as it divided the BUD cohort into two sub-groups of 10 patients that were comparable in terms of gender ratio and category of lesion (Table  1 ). O-PLS-DA of serum proteins resulted in a clear separation between FH and SH (R 2 = 1. 00; Q 2 = 0. 43) (Fig. 6A ), but not between FH or SH between week 0 and week 12 (unshown data). Compared to FH, SH displayed relatively higher initial levels of TNF-b and MCP-1; and lower levels of Factor VII and Apo H (Fig.
Funding for this work was provided by the  European Foundation Initiative on NTD  grant  I/83994 , the  European Community 's  Seventh Framework Programme  under grant agreement nu  241500 , the  MRC/DFID African Research Leader  grant  MR/J01477X/1 , the '' Association Raoul Follereau'' and the ''Fondation pour la Recherche Medicale '' [Equipes FRM 2012]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Background: Analyzing time-to-onset of adverse drug reactions from treatment exposure contributes to meeting pharmacovigilance objectives, i. e. identification and prevention. Post-marketing data are available from reporting systems. Times-to-onset from such databases are right-truncated because some patients who were exposed to the drug and who will eventually develop the adverse drug reaction may do it after the time of analysis and thus are not included in the data. Acknowledgment of the developments adapted to right-truncated data is not widespread and these methods have never been used in pharmacovigilance. We assess the use of appropriate methods as well as the consequences of not taking right truncation into account (naive approach) on parametric maximum likelihood estimation of time-to-onset distribution.
Results: The simulation study shows that the bias and the mean squared error might in some instances be unacceptably large when right truncation is not considered while the truncation-based estimator shows always better and often satisfactory performances and the gap may be large. For the real dataset, the estimated expected time-to-onset leads to a minimum difference of 58 weeks between both approaches, which is not negligible. This difference is obtained for the Weibull model, under which the estimated probability of this distribution falling within an observable values interval is not far from 1. Conclusions: It is necessary to take right truncation into account for estimating time-to-onset of adverse drug reactions from spontaneous reporting databases.
Early estimation procedures tailored to available pharmacovigilance data, i. e. spontaneous reporting data, should be sought. The data consisting of the time-to-onset among patients who were reported to have potentially developed an adverse drug reaction are right-truncated. Truncation arises because some patients who were exposed to the drug and who will eventually develop the adverse drug reaction may do it after the time of analysis (Figure  1 ). Among patients exposed to the drug, only those who Figure  1  Right truncation and data on time-to-onset of adverse drug reactions from spontaneous reporting databases. Some patients who were exposed to the drug and who will eventually develop the adverse drug reaction may do it after the time of analysis. Here, in these hypothetical examples, the patient on the top line is included in the database because he experienced the adverse drug reaction before the time of analysis, i.
e. x 1 t 1. The patient on the bottom line is not included in the database because he has not yet experienced the adverse drug reaction, i. e. x 2 t 2 , when data are analyzed. experienced adverse reactions before time of analysis are included in the database. No information is available for the other patients. If all the patients begin their treatment at the same time, the data are right-truncated with a single truncation time. If they do not all begin their treatment at the same time, the data are right-truncated with different truncation times. In spontaneous reporting, data are right-truncated with different truncation times and they require appropriate statistical methods. This paper investigates parametric maximum likelihood estimation of the time-to-onset distribution of adverse drug reactions from spontaneous reporting data for different types of hazard functions likely to be encountered in pharmacovigilance.
Acknowledgment of the developments adapted to right-truncated data is not widespread and these methods have never been used in pharmacovigilance. No simulation studies are available on the accuracy of their estimates. Furthermore, a naive approach that does not take into account right truncation features of spontaneous reports and uses classical parametric methods instead of appropriate methods may lead to misleading estimates. We consider the two approaches, i. e. taking or not taking right truncation into account, and the corresponding parametric maximum likelihood estimators. Both approaches are compared with a simulation study conducted to evaluate the consequences, notably in terms of bias, of not considering right truncation on the maximum likelihood estimates, as well as assessing the performances of the right truncation-based estimation.
Application study We analyzed 64 French cases of lymphoma that occurred after anti TNF-α treatment using the national pharmacovigilance database at the date of February 1, 2010  [25]. The population included patients suffering from rheumatoid arthritis, Crohn's disease, ankylosing spondylitis, psoriatic arthritis, psoriasis, Sjögren's syndrome, dermatomyositis, polymyositis or polyarthropathy and exposed to one or (successively) more of the three anti TNF-α available at the study date: etanercept, adalimumab and infliximab. The occurrence of a malignant lymphoma was confirmed by histopathological analysis. Marketing authorization was obtained in August 1999 for infliximab, in September 2002 for etanercept and in September 2003 for adalimumab. These 64 adverse effects occurred between July 2001 and October 2009.
None of the survival or truncation times was missing in the database. The observed maximum truncation time was 529 weeks. All anti TNF-agents taken together, we derived the parametric maximum likelihood estimates and secondarily corresponding estimated mean times, with and without considering right truncation, for the exponential, Weibull and log-logistic distributions. For completeness, we also derived the non-parametric maximum likelihood estimation. The French pharmacovigilance database is developed by the French drug agency (Agence Nationale de Sécurité du Médicament et des produits de santé, ANSM) and is not publicly available. It is build up and used on an ongoing basis by the network of regional pharmacovigilance centres, which have a direct access to the data. This set of data has already been extracted for another study  [25]  with the authorization of the ANSM and the network of regional centres, according to the internal rule.
The Nomenclature Committee of IUBMB discussed ways of incorporating structural information in the enzyme list in a systematic way, i. e. going beyond what are little more than anecdotal notes in the Comments. Nothing was ever agreed or implemented, however, but fortunately the web-based list includes links to databases such as EXPASY, thus allowing structural information to be combined with reaction information. The original classification scheme remains very satisfactory for the enzymes of central metabolism, but there have always been some problem groups, most notably the peptidases, and the wholesale reorganization of group 3. 4 in 1972 reflected the difficulties. The primary problem is that although the enzymes of central metabolism have sufficient specificity for reaction to be defined with some precision, many peptidases have broad and overlapping specificity.
In addition, the fact that the peptidases constituted a much higher proportion in 1961 than now of the enzymes that had been studied meant that numerous enzymes that differ mainly in being derived from different organisms have been classified as different enzymes with different EC numbers. For example, papain (now EC 3. 4. 22. 2), ficain  (EC 3. 4. 22. 3) , asclepain (EC 3. 4. 22. 7), actinidain  (EC 3. 4. 22. 14)  and stem bromelain  (EC 3. 4. 22. 32) all have very similar catalytic properties. Classifying the overlapping specificity of peptidases (many more of which are known today than there were at the time of the original Report  (IUB, 1961) ) is now more efficiently covered by a dedicated database  (Rawlings et al. 2012 ). 9 t the other extreme are the enzymes of the restrictionmodification systems.
For example, EC 1. 1. 1. 113 contains the enzymes collectively known as site-specific DNA-methyltransferase (cytosine-N 4 -specific). This is actually a large group of enzymes, each clearly distinct, that recognize specific sequences of DNA. Although it would be technically possible to classify these in the same way as the enzymes of central metabolism, giving each one a separate name and EC number, the purpose is better served by referring to specialized databases such as REBASE  (Roberts et al. 2010 ). 10.
, D-mannose, D-fructose, sorbitol and D-glucosamine can act as acceptors; ITP and dATP can act as donors. The liver isoenzyme has sometimes been called glucokinase. Systematic name: ATP:D-hexose 6-phosphotransferase Links to other databases: BRENDA, EXPASY, GTD, IUBMB, KEGG, METACYC, PDB, UM-BBD, CAS registry number: 9001-51-8.
S. PAU is a population representative of the general white population  [8]. Clinical data regarding family history are not collected in the MARTHA population. The hypothetical patient follows the sequence shown in the decision tree (Fig. 1 ). The evolution of the patient ends when the patient dies from VTE or survives VTE with or without bleeding due to warfarin therapy. It was considered that the patients included in the model would be aged 45 years (the mean age of the patients in the S. PAU and Patients with potential thrombophilia Fig. 1  Model used for evaluating the efficiency of Thrombo inCode Ò (TiC), a clinical-genetic function for estimating the risk of venous thromboembolism (VTE) in patients with potential thrombophilia, versus the standard method used to date (factor V Leiden and prothrombin G20210A)  [10].
High Content Analysis Plates were scanned with the Thermo Scientific ArrayScan VTI HCS Reader (Thermo Scientific, Waltham, MA, USA) and analyzed using the Target Activation module of the BioApplication software (Thermo Scientific, Waltham, MA, USA). For each well, 10 fields (10× magnification, white bar = 100 μm) were scanned and analyzed for quantification of immunofluorescence. Cell viability was determined by cell counting from DAPI staining and expressed as a percentage of cells compared to vehicle control condition. Nuclear DAPI staining was used to analyze cell cycle phases using the module Cell Cycle Analysis of the BioApplication software (Thermo Scientific, Waltham, MA, USA). Caspase-3 activation was quantified in the whole cell and expressed as a fold increase compared to untreated cells.
ACKNOWLEDGEMENTS The authors are thankful to  A. Regragui ,  Department of Biology, Laboratory of Animal Physiology, laboratory of Ecopharmacology, Neurobiology, and Behavior  for his technical assistance. The authors are also grateful to  F. Megraud ,  Centre National de Référence des Helicobacters et Campylobacters. Laboratoire de bactériologie, hôpital Pellegrin ,  Université Victor Segalen ,  Bordeaux 2. Bordeaux ,  France , and all laboratory members for their cooperation and assistance for the anti-Helicobacter pylori test.
In contrast, proliferative responses and cytokine secretion was not detected with cells from vehicle control mice. Flucloxacillin-specific hepatocyte toxicity and apoptosis was observed when CD8+ T-cells were cultured with dendritic cells and flucloxacillin for 24h, washed and transferred to the hepatocyte cultures. Flucloxacillin sensitization followed by 10 day oral exposure resulted in marked gall bladder swelling and mild elevations in ALT. Other plasma biomarkers of liver damage were negative. In conclusion, these data show successful sensitization of mice against flucloxacillin. Thus, the C57/Bl6 CD4 deficient mouse represents a promising model to study the role of the adaptive immune system in DILI.
At this step, none of the students required any clarification regarding the questions (Step 1). The survey was then disseminated to an additional sample of 20 medical students to evaluate how thoroughly the items were completed. Because we observed that certain questions had been left blank at this step, we modified the questionnaire to prevent respondents from skipping any questions (Step 2). In June 2012, we sent an email, followed by two reminders, to the dean of each medical school in France (n = 37 deans contacted), requesting that they send the link to the online survey to all of their students through their intranet/email databases (Step 3). We closed the survey in December 2012 for the extraction of data and the analyses. Participation was voluntary and anonymous. All medical students were eligible to participate: preclinical (first three years of medical school), clinical (years 4 to 6), and residents (years 7 to 10).
Only students who received an e-mail from the dean of their medical school were potential participants; however, the exact number of students contacted by their dean is unknown. No formal informed consent was required. The local responsible of Commission Nationale Informatiques et Libertes (CNIL) indicated that we were not required to declare the survey to the French authority that oversees the protection of personal data. All statistical analyses were conducted using the SAS Statistical Package. The mean and standard deviation (SD) were given for the continuous variables. Chi-square tests were used for the comparison of categorical variables. Logistic regression was performed to assess the effects of selected variables on the perceived potential influence of COI on prescriptions (for self and for others).
0001). Nevertheless, few (21. 4%) felt that attending a meal sponsored by the pharmaceutical industry represented a possible COI. Regarding disclosure of COI to patients or in a public database, fewer residents favored more transparency as compared to preclinical/clinical students. We performed a logistic regression analysis to test for an association between personal research on and lectures about COI on two items that assessed the perception of potential induced bias in prescriptions for others and for self. Personal research on COI was more likely to be associated with perceived bias for self (but not for others) than attending a lecture on COI (Table  3 ).
Characterization of protein-loaded nanoparticles: The morphology of the nanoparticles was performed by transmission electron microscopy (TEM, JEOL, JEM1400, Japan). Drops of nanoparticle suspension diluted in ultrapure Milli-Q ® water were deposited in carbon-coated copper grids and negatively stained with 1% phosphotungstic acid solution, the excess solution was blotted off using filter paper, and the grids were air dried before observation. Nanoparticle size was determined by dynamic light scattering and zeta potential was measured by electrophoretic light scattering principle using a Nanosizer ® ZS (Malvern Instruments, Worcestershire, UK). Suspensions of nanoparticles diluted in ultrapure water to a suitable concentration were used for these analyses. Data analysis was performed in automatic mode at 25˚C.
Structural integrity of lysozyme after encapsulation: Different spectroscopic techniques were used to reveal any changes in the protein structure after encapsulation. Due to the detection limit of these methods (0. 06 mg/ml), only structure integrity of lysozyme was assessed. Circular dichroism spectroscopy: Circular dichroism (CD) spectra were recorded on a J-810 CD spectrometer (Jasco, Japan) in step mode (bandwidth: 2 nm; interval: 0. 1 nm; response time: 0. 125 s). The CD data were collected from 200 to 260 nm using a mini-quartz cell (Hellma, Germany) with 0. 2 cm path length and averaged over 5 scans to increase the signal to noise ratio. The temperature of measurement was controlled with a Peltier-effect temperature controller, and was usually 20°C. At least three CD spectra were monitored for each sample and were scaled in units of milli-degrees (mdeg).
The spectra were then averaged and smoothed using a 13-point fast fourier transform (FFT) algorithm (spectra manager software). UV-spectroscopy in the fourth derivative mode: Baselinecorrected absorbance spectra in the range of 260-330 nm were recorded at 37°C using a V-530 Jasco UV/Vis spectrophotometer. Data acquisition was in steps of 1 nm with an acquisition time of 1s per data point. The fourth derivatives of the UV spectra were calculated with Spectra Manager analysis software (Jasco, Japan) to improve the low resolution of zero-order spectra and provide information about structural changes of proteins in the local environment of tyrosine and tryptophan residues. Fluorescence spectroscopy: Spectra were recorded on a FP-6500 spectrofluorometer (Jasco, Japan) equipped with a Peltier temperature controller.
The emission spectra were measured (bandwidth: 5 nm: response time: 0. 5 s; scan rate: 100 nm/min; data pitch: 0. 1 nm) in a 0. 2 x 1 cm mini cell (Hellma, Germany) at 25°C. The excitation wavelength was 295 nm for selective excitation of the tryptophan residue. The spectra were measured three times from 310 to 450 nm and the data were averaged to increase the signal-to-noise ratio. All the spectra were corrected for the Raman signal and background by subtracting the spectrum of the vehicle solution. Residual glycofurol quantification: Residual glycofurol content was analyzed in blank PLGA nanoparticles using a previously described protocol  [41]. The entire batch was first dissolved in 1 ml of acetone (3 batches, 3 experiments per batch). The polymer was then precipitated by 4 ml of distilled water.