dataesr/scientific-paragraphs-categorization huggingface dataset:
These results are discussed in the supplemental material. Finally, the simulation was validated by varying the multiplicity of infection (MOI) at time of treatment, the relative detectability of alleles based on length, and the minority FIG  5  The true status of recurrent infections on each day of follow-up for a simulated trial of DHA-PPQ with a true failure rate of 12% and an FOI of 8. The multiplicity of infection (MOI) is drawn from data from Tanzania-a relatively high-transmission area. The total height of the bars indicates the number of recurrent infections detected on that day of follow-up, and the color coding shows the number of those recurrent infections that were truly recrudescent or reinfections. allele detection threshold. The results of these analyses are provided in the supplemental material and showed mostly the same qualitative patterns as the results presented above; the one key departure was that assumption of a minority allele threshold of 5% (reduced from the assumption of 25% presented above) led to slightly increased failure rates and the no-glurp algorithm being the most accurate at a moderate to high FOI.
This was not ideal, but there is no obvious alterative given that key parameters (including the vital one-true failure rate), cannot be directly observed in vivo. Confidence in this approach was ensured given the past success of pharmacological modeling to correctly reflect and predict clinical data (e. g. , see references 4 and 14 to 18) and the consistency of the simulated results with in vivo Rwandan and Cambodian data sets. We acknowledge that our model may not reflect the in vivo parameters of these trials (though see the discussion for the parameter space we covered in the supplemental material); however, the purpose of reanalysis of these data was to investigate the change in failure rates from use of proposed algorithms on in vivo data-analysis of trial results with these algorithms had not previously taken place.
msp-1 and msp-2 allelic frequency distributions and amplicon sizes were derived from 115 or 108 patients from Tanzania  (30). glurp distributions were drawn from a collection of field samples described in reference 11. The length of each allele and its allelic family (for msp-1 and msp-2) were also noted. The distributions we used gave msp-1 an expected heterozygosity (He) of 0. 915, msp-2 an He of 0. 963, and glurp an He of 0. 956 (see Data Set S1 in the supplemental material for full data). It was assumed that the genotypes of initial clones were independent of each other and independent of the genotypes of reinfections (i. e. , it was assumed there is no local genetic structuring of the malaria population). Note that alleles at msp-1 and msp-2 exist in our distributions as members of three or two distinct families, respectively.
The estimator at the final time point (i. e. , the last day of follow-up) was the probability that their treatment was considered a "success" at the end of the trial. Consequently, its complement gave the probability that a given individual will fail treatment: F ^ϭ 1 Ϫ S ^͑t ͒ The final methodological step was to interrogate the modeled data to determine the "true failure rate"-i. e. , the drug failure rate calculated directly from the parasitemia of each patient (thus, not dependent on genotyped data). For each patient in the simulation, an outcome on the final day of follow-up was determined: if, on the final day, the patient had any parasites from any initial clones (i. e. , even a single parasite), the patient was denoted as a drug failure. If no parasites had survived from the initial clones present at treatment, that patient was denoted as a treatment success.
The true failure rate, F, for the patient population was then calculated as F ϭ f N where f was the number of drug failures on the final day of follow-up and N was the total number of patients. This was the "gold standard" metric and cannot be obtained in vivo. It was compared to the estimated failure rates obtained from modeling the clinical trial and molecular correction process and allowed us to quantify the accuracy of different methods (i. e. , their ability to recover the true failure rate). Reanalysis of existing in vivo data. Clinical data were obtained from Rwanda (a relatively hightransmission area) across 6 sites between 2013 and 2015, where patients were treated with either AR-LF or DHA-PPQ and genotyped at msp-1, msp-2, and glurp. In patients treated with AR-LF, 137 recurrences were observed, of which 110 could be classified as either a reinfection or a recrudescence.
(It was not possible to classify 27 patients because they had incomplete genetic data. ) In patients treated with DHA-PPQ, 48 recurrences were observed, of which 43 could be classified as either a reinfection or a recrudescence. (It was not possible to classify 5 patients because they had incomplete genetic data. ) These data were initially presented internally to the National Malaria Control Program in Rwanda (unpublished data). Clinical data from Cambodia (a relatively low-transmission area) were obtained from 6 sites between 2014 and 2016. Patients were treated with either artesunate plus amodiaquine (AS-AQ), artesunate plus pyronaridine (AS-PYN), or DHA-PPQ and genotyped at msp-1, msp-2, and glurp. In patients treated with AS-AQ, 12 recurrences were observed, of which 5 could be classified as reinfection or recrudescence.
ACKNOWLEDGMENTS We thank  Leang Rithea  for collecting and facilitating access to field data from Cambodia. We thank  Jörg Möhrle  and  Stephan Duparc  for discussion and for collating the data presented in Table 3. We thank  Pascal Ringwald  for many helpful comments and for facilitating access to field data. We thank two anonymous reviewers for helpful and thoughtful commentary on the manuscript. The authors have no conflicts of interests to declare. This project was supported by the  Medical Research Council  (grants  G1100522  and  MR/L022508/1 ), the  Bill and Melinda Gates Foundation  (grant  1032350 ), and the  Malaria Modelling Consortium  (grant  UWSC9757 ). The funding source(s) had no role in study design, collection, analysis, or interpretation of data, the writing of the report, or the decision to submit the paper for publication.
In vivo monitoring of the unlabelled thermogel by MRI Experiments were carried out at 7 T on a vertical imaging spectrometer fitted with an ultra shielded refrigerated 300WB magnet (Bruker, Avance II, Wissembourg, France) equipped with a whole-body 40 mm birdcage RF coil (Bruker) for mouse and Paravision 5. 1 acquisition software. For in vivo experiments, the mouse was positioned in a specific craddle maintaining the head via a face mask, which provided volatile anesthesia. The mice were anesthetized with 1. 3% isoflurane (TEM, Bordeaux) in a mixture of oxygen-air (1:2, 1 L/min). The animal's temperature was monitored and controlled. Breath triggering was performed using a plastic sensor located on the mouse's thorax and the physiological monitoring for respiratory gating (SA Instruments.
In vivo monitoring of the labelled thermogel by optical imaging Mice bearing CT26 tumor were anesthetized by i. p injection of a mixture 10 mg/kg Xylazine and 80 mg/kg Ketamine. Cy5-T-gel (60 µL) was injected on the left tumor whereas Cy5-MT-gel (60µL) was injected on the right tumor. Fluorescence signal was acquired with an epifluorescence system (Fluobeam camera, Fluoptics, France) at various time points post-injection. The signal was quantified over a region of interest (ROI) applied to the tumors using Image J software. Results were expressed as the percentage (%) of the signal taken at t0 as regard to the percentage (%) of the signal at at time t, according to the formula: % = ×.
Quantification of T cells density Each slide was examined using a microscope. The lymphocyte density was quantified on ten representative images realized with Leica Digital Microscopes (Leica DM 4000 B,Wetzlar, Germany) at x 20 magnification, with the help of image J software  (Schneider et al. , 2012 ). An original macro was created, which was dedicated to segment histologic parameters on the basis of contrast and color. Thus from original picture, a color deconvolution with DAB vectors was apply and the next step of analysis was performed on the red chanel obtained. This image was converted in 8-bit and the brightness and contrast was setting from 60 to 100. After binarization of the successive images 3 process was applied "Dilate, Fill Holes and Watershed" then the analysis of particles from 15-200 µm² (FOXP3) or 20-200 µm² (other cell type) was made.
Statistical analysis Graph Pad Software was used to analyze data and determine statistical significance between groups. Data are shown as the means ± SEM. Mann-whitney test was used to compare the difference between two groups. One way anova test with Bonferroni posttest was used for mutiple comparaisons P values < 0. 05 were considered significant. The symbol meaning : ns non significant P > 0. 05, * P ≤ 0. 05, ** P ≤ 0. 01, *** P ≤ 0. 001.
In vivo mucoadhesion In order to evaluate if mucoadhesion data obtained by rheometry could reflect a mucoadhesion in vivo. We performed non-invasive longitudinal monitoring of the gel by MRI. T-gel and MT-gel were injected in CT26 tumors grafted in the flank of mice. We showed in Fig. 3  that the gel could be followed thanks to an hypersignal obtained by T2-MRI. The signal related to the water confined within the gel was therefore taken as a signature of the thermogel. By this method, we could evidence the effect of the mucoadhesive agent on the gel. Indeed, MT-gel could be detected longer within the tumor area than T-gel. Two time-points illustrate this observation (Fig. 3 ). The difficulty in this experiment is that the signal of water is followed and multifactors might occur, the release of water but also the swelling of the gel.
Acknowledgements The authors would like to thank the LIOPA (the small animal optical imaging platform), the Animal facility of the faculty of pharmacy and ICM (cellular and molecular imaging platform) from  UMS 3612 CNRS, Paris Descartes University, France. In vivo imaging was performed at the  Life Imaging Facility of Paris Descartes University (PIV, Plateforme Imageries du Vivant ), supported by  France Life Imaging  (grant  ANR-11-INBS-0006 ), the  Infrastructures Biologie-Santé  and the  GDR-AIM. The authors thank  Olivia Bawa  from  ' Unité de pathologie expérimentale de l'IRCIV, Gustave Roussy, Villejuif, France ' for IHC staining.
Biological Efficiency (BE) was calculated as the fresh weight of fruiting bodies (g) produced on 100 g (dry weight) of substrate. Production Rate (PR) was estimated as the biological efficiency per day of production. The experimental design was completely random, consisting of six treatments: a substrate inoculated with P. polymyxa and a control substrate on days 0, 3 and 5. Ten replicates were conducted for each treatment. The BE and TP were subjected to an ANOVA and differences in the means identified by Tukey post hoc multiple range test (α = 0. 05%) with Statistica® software ver. 6. 1.
For the confrontations, 10 g of straw from each treatment were placed in a Petri dish under sterile conditions. One barley grain of spawn of P. pulmonarius was placed at 1 cm from the border and, 1 cm from the edge at the opposite side, a T. viride implant (0. 7 cm Ø) developed in agar-water was placed. Ten replicates were conducted per treatment. For each treatment, the mycelial growth of P. pulmonarius and T. viride was measured, estimating the area occupied by each on days 3, 5 and 7 of incubation at 25 °C. The areas occupied were digitized and measured using Adobe Photoshop CS3 extended 10. 0 software. The data were subjected to an ANOVA and the difference in the means identified by Tukey post hoc multiple range test (α = 0. 05%) with Statistica® software ver. 6. 1.
5 tandem conjugation kit to goat IgG isotype control antibody, Innova Biosciences). Compensations were calculated by acquisition of fluorescent beads. Both percentage and geometric means of fluorescence were recorded. The results were expressed as the percentage of labelled positive cells. A FACS DiVa flow cytometer (BD Biosciences) equipped with an argon ion and He-Ne lasers was used. Data were analyzed using DiVa 5. 03 software.
The insert fragments were finally placed on glass slides, submerged in Vectashield Mounting Medium (H-1000, Vector Laboratories) and covered with glass coverslips. Evaluations of each marker were repeated at least twice. All biomarkers were imaged using an inverted epifluorescence microscope IX81 (Olympus, Tokyo, Japan) equipped with CellSens imaging software (Olympus, Munster, Germany). To highlight the spatial localization of tight cell junctions and ABC transporters in MucilAir™ tissue, a FLUOVIEW FV1200 laser scanning confocal microscope (Olympus, Tokyo, Japan) equipped with FV10-ASW4. 1 imaging software was used.
Mass spectrometry (MS) was performed using a Q-Exactive Plus spectrometer (ThermoFisher, San Jose, California, USA). After measurement of a precursor scan of intact peptides in the Orbitrap by scanning from m/z 375-1500 (with a resolution of 70,000), the 10 most intensely multi-charged precursors were selected for HCD analysis in the C-trap (with a resolution of 17,500 and a normalized collision energy of 27). Automatic gain control (AGC) targets were 3 x 10 6 ions for MS scans and 1 x 10 5 ions for MS/MS scans. The maximum injection time was 100 ms and dynamic exclusion for 30 s was used to reduce repeated analyses of the same components. Data concerning the identification and quantification of peptides and proteins were analyzed using Proteome Discoverer ® software version 2. 0 (Thermo Fisher Scientific).
The animals had free access to water and were fed ad libitum. The experimental trial lasted 56 days. Piglets were weighed at the beginning and at the end of the trial. The health status of the piglets was recorded daily. The amounts of feed offered and refused were recorded daily to calculate the feed intake. These data were used to calculate the average daily gain (ADG), gain feed ratio (G:F), and average daily feed intake (ADFI) of each pen. sample collection Blood samples were obtained, by anterior vena cava puncture before the morning feeding, from 10 randomly selected castrated male piglets per treatment (2 piglets/ pen), at the beginning of the trial, and at days 14 and 56. The blood samples were collected in 10 mL vacutainer glass tubes (Venoject®, Terumo Europe N. V. , Leuven, Belgium) and immediately placed on ice.
statistical analyses All parameters were analyzed using SPSS (SPSS/PC Statistics 24 SPSS Inc. , IBM). Live weight and immunoglobulin data were analyzed by GLM (General Linear Model) procedure with treatment as the main effect and the value at the beginning of the trial entered as a covariate. Differences in means were compared according to Bonferroni Multiple Comparisons. Biochemical parameters and serum oxidative status were analyzed by repeated measure analysis of variance (ANOVA) to assess the main effect of treatment and time and value at the beginning of the trial entered as a covariate. Pen was the experimental unit for the productive performance. Piglet was considered the experimental unit of all serum metabolites and immunological variables. Data are presented as means±SEM, and a value of P≤0.
In order to support pharmacovigilance legislation, including regulatory guidance on PASS, the EMA launched in 2007 the European network of centres for pharmacoepidemiology and pharmacovigilance (ENCePP), with a key aim of supporting principles of transparency, scientific independence, and common quality standards in pharmacoepidemiological research  [23]. However, important challenges remain, including the long duration required to finalize the studies. The ENCePP is expected to come out with innovative tools, designs, and new big data sources to conduct faster studies through collaborations  [24, 25]. A suggested solution is to facilitate access to big data registries. The power of such databases in pharmacovigilance is greatly promising  [26, 27]. As example, the French national health insurance database [système national des données de santé (SNDS)] contains individualized and anonymous administrative and medical data for more than 99% of French residents.
Furthermore, since 2006, an anonymous unique identifier for each patient links to the national hospital discharge database [programme de médicalisation des systèmes d'information (PMSI)], which provides reasons for admission (in the format of the ICD-10), medical procedures performed during the stay, and comorbidities leading to need for care during hospital stay  [26, 28]. Other very large databases or database systems include the sentinel in the United States, the Canadian network of provincial claims databases (CNODES) in Canada, and the national patient register at Denmark  [26, 29]. It is worthy to investigate the impact of such system on the improvement of the production and quality of conducted PAS. The enrichment of health databases and their computerization represent an opportunity to respond to the authorities' queries.
In order to facilitate access to these databases and optimize their use, it was recommended to facilitate their evolution and to guarantee their technical validity  [30]. In conclusion, in parallel to the challenges found in the field of pharmacovigilance especially those pertaining to the efficiency and organization of this system, the detection of signals, and the rising power of computer networks  [31] , pharmacoepidemiology and PAS are useful tool to complement pharmacovigilance regulatory activities with its own limits  [32]. Access to big data sources is expected to foster the implementation and completion of PAS, and facilitate the harnessing of their potential  [33]. Indeed, big data have a very limited value per se, however when critically evaluated, correctly analyzed and interpreted they could be useful in supporting and guiding regulatory decisions on drugs  [34, 35].
Results Clinical demographics of patients and controls. We recruited a total of 73 patients of SCAR, including 8 cases with CBZ-DRESS, and 65 with SJS/TEN caused by carbamazepine (CBZ) (n = 42), oxcarbazepine (OXC) (n = 3), lamotrigine (LTG) (n = 4), phenytoin (PHT) (n = 6), and allopurinol (ALP) (n = 10). The demographics and HLA genotype data of patients are listed in Supplementary Tables  1  and 2. Among the 42 cases of CBZinduced SJS/TEN, HLA-B*15:02 allele was found in all 24 (100. 00%) of Chinese, 4 of 5 (80. 00%) Thai patients, and 2 of 13 (15. 38%) subjects enrolled from Europe (Supplementary Table  1 ). In addition, all three patients with OXC-SJS carried HLA-B*15:02 (Supplementary Table  2 ). We also enrolled drug-tolerant controls (n = 12) who had taken CBZ for more than 6 months without adverse reactions, and six of them carried HLA-B*15:02 (Supplementary Table  3 ).
81 and 42. 85%, respectively, in a representative data set (Supplementary Fig. 6 ). To validate the TRVA/ TRVB pairing and gene expression profile, we sorted 30 single cells using flow cytometry with monoclonal antibodies (mAb) against TRBV12-3/TRBV12-4. Then the paired TRVA/TRVB, and 18 phenotypic genes were determined by single-cell sequencing. All (100%) of the 30 cells expressed the same specific TCRβ CDR3 clonotype "ASSLAGELF", and 25 (83. 33%) cells expressed the TCRα CDR3 clonotype "VFDNTDKLI" (Fig. 4a, b ). Two other TCRα CDR3 clonotypes "AASPPDGNQFY" and "ALDIPNFGNEKLT" were found to pair with the specific TCRβ, but presented in low frequencies (13. 33 and 3. 33%, respectively) in the blister cells (Fig. 4a, b ). The expression of the specific TCRɑ   6  and Supplementary Fig. 7 ).
The CDR3 interval of TCR transcripts was identified as comprising all the amino acids between the Y[YFLI]C at the 3′ end of the V gene segment and [FW]GXGT (X represents any amino acid) within the J segments  56. 7] [58]. Flow cytometry. Flow cytometry was carried out using distinct fluorochromeconjugated mAb that recognize human CD4, CD8, CD56 (Beckman Coulter), CD45RA, CD197 (BioLegend), human TRBV12-3/TRBV12-4 (Beckman Coulter), and mouse CD4 and CD8 (eBioscience). These mAbs were labeled with Alexa Fluor 488, phycoerythrin (PE), phycoerythrin-Texas Red (ECD), phycoerythrincyanin 5 (PC5), or phycoerythrin-cyanin 7 (PC7). The cells were examined by means of multicolor flow cytometry on the Cytomics FC500 flow cytometer (Beckman Coulter), and data were analyzed with CXP software (Beckman Coulter).
Statistical analysis. Significant differences between the groups were analyzed using an unpaired, two-tailed Student's t test. The heatmaps were generated using the built-in R heatmap() function in stats package, the Circos plots by VDJtools software (MiLaboratory), and the treemaps by the treemap() function version 1. 2. 0. 1 (MATLAB Central File Exchange), respectively. Graphs were produced using Graphpad Prism (version 7. 02), and the data are shown as mean ± standard error of the mean (s. e. m. ) unless stated otherwise. The results were considered statistically significant when P ≤ 0. 05. Significance levels were ns; *P < 0. 05; **P < 0. 01; ***P < 0. 001; ****P < 0. 0001 as suggested by Graphpad Prism. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank the support of members of the  Whole-Genome Research Core Laboratory of Human Diseases , and the  Cancer Vaccine and Immune Cell Therapy Core Laboratory ,  Chang Gung Memorial Hospital, Taiwan. This work was supported in part by grants from the  Ministry of Science and Technology, Taiwan  ( MOST 102-2314-B-010-014-MY3 ,  103-2321-B-182-001 ,  104-2314-B-182A-148-MY3 ,  104-2325-B-182A-006 ,  104-2320-B-010-036-MY3 ,  105-2628-B-010-007-MY3 ,  106-2314-B-182A-037-MY3 ), and  Chang Gung Memorial Hospital  ( CLRPG2E0051~3 ,  CORPG3F0041~2 ,  OMRPG3E0041 ,  CMRPG1F0111~2 ,  CORPG3F0061~2 ,  CIRPG3I0041 ). Most funds for the European RegiSCAR group came from a research grant from the  European Commission  ( QLRT-2002-01738 ). Maja Mockenhaupt received the  Else Kröner Memorial Stipendium  for support of clinical research through the  Else Kröner-Fresenius-Foundation.
Data availability The source data underlying Figs. 1a, 1b, 2a, 2c, 2f, 3a, 3b, 3c, 3d, 3e, 3f, 4b, 4c, 5, 6a, 6c, 6d, 7f-j, 7k-m, and Supplementary Figs. 1, 2, 7, 8, 10 are provided as the Source Data file. The sequence data that support the findings of this study have been deposited in the NCBI sequence read archive (SRA) database with links to BioProject accession ID PRJNA550004. All other data are available from the authors upon reasonable requests.
High Performance Thin Layer Chromatography (HPTLC) Analytical grade solvents (acetone, dioxane, hexane, and methanol) were from Sigma-Aldrich. All analyses were performed with Merck (Darmstardt, Germany) silica gel 60 F254 plates (20 × 10 cm), using a Camag (Muttenz, Switzerland) HPTLC system equipped with an automatic TLC sampler (ATS 4), an automatic developing chamber (ADC 2), a visualizer, and a TLC scanner 4 controlled with winCATS software (version 1. 1. 10, CAMAG, Muttenz, Switzerland). Standards and sample solutions were applied as bands (length of 8 mm, 250 nL/s delivery speed, track distance 8. 0 mm, and distance from the edge of 15 mm). The 10-mL mobile phase used to develop the plates was hexane:dioxane (8:2 v/v) with a migration distance of 80 mm at room temperature after 30 s of pre-drying and no tank saturation.
Visual documentation of the plates was carried out at 254 nm and 366 nm. The plates were then scanned in reflectance mode at 240 nm (for M, DHM, K and DHK) and at 355 nm (for Y, DMY, FKA, FKB, FKC) with D2 and W lamp slit dimension 8. 00 mm × 0. 20 mm, scanning speed 20 mm/s, and data resolution 100 µm/step. Peak area measurements (in area units, AU) were used. The total analytical time was 50 min for 20 samples and 10 mL of mobile phase (corresponding to 2. 5 min and 0. 5 mL per sample).