dataesr/scientific-paragraphs-categorization huggingface dataset:
37. The strain was sent to the French Yersinia National Reference Laboratory (Institut Pasteur, Paris, France), where tests using API20E and 50CH strips, Tween-esterase, pyrazinamidase activity, and O-antigen seroagglutination revealed that the strain (IP39285) belonged to the Y. enterocolitica bioserotype 1B/O:7,8-8-8,19. Since isolation of biotype 1B strains is extremely rare in France, polymerase chain reaction (PCR), targeting yadA (located on the Yersinia virulence plasmid) and fyuA (located in the Yersinia high-pathogenicity island), was performed. Both PCR tests were positive, confirming that IP39285 was a highly pathogenic biotype 1B strain. Multilocus sequence analysis (glnA, gyrB, hsp60, and recA)  14  showed that this strain belonged to the same branch as other biotype 1B strains (data not shown).
The annotated genome of the IP39285 strain was deposited to GenBank, and is available under BioProject PRJNA407758 (accession number NWMR00000000). The genome was annotated with the NCBI Prokaryotic Genome Annotation Pipeline (PGAP), and consists of 4,325,608 bp in 143 contigs, with a G + C content of 47. 5%. PGAP found a total of 4061 genes, 3988 coding sequences (CDSs), 73 RNA genes, and 169 pseudogenes. The G + C content and the number of CDSs are similar to those of other publicly available genomes of biotype 1B Y. enterocolitica strains. In silico genomic analysis revealed the presence of classic virulence genes, including ail, inv, ystA, and myfA, located on the chromosome; fyuA and irp2, located in the Yersinia highpathogenicity island; and yadA and virF, located on the Yersinia virulence plasmid.
Prediction of antimicrobial susceptibility genes in the IP39285 genome using ResFinder (http://www. genomicepidemiology. org/) allowed the identification of the blaA gene, responsible for beta-lactam resistance, as in all other biotype 1B strains, and of the vatF gene, responsible for streptogramin B resistance, as in 14 out of the 18 other biotype 1B genomes tested. We then conducted a comparative genomic analysis to identify single-nucleotide polymorphisms (SNPs) with the wgSNP analysis module of Bionumerics 7. 6 (Applied Maths, Sint-Martens-Latem, Belgium) using the genome of strain 8081 as a reference (accession number AM286415) (Fig. 1 ). In total, 25,671 SNP positions were identified in the 19 compared strains, highlighting a high genetic diversity among these isolates (Fig. 1 ). For comparison, in 264 Y.
NMR spectra NMR spectra were recorded using a Bruker Avance-400 spectrometer equipped with a 5 mm single-axis Z-gradient quattro nucleus probe, operating at 100 MHz for 13 C NMR. The spectrometer was operated with TOPSPIN NMR software (Version 2. 0). Chemical shifts (d) are reported in parts per million (ppm), relative to (CH 3 ) 4 Si (0. 00 ppm) for 13 C-NMR spectra.
Acknowledgements We thank  Anthony Archer ,  Gemma Chapman  and  Jamil Gregory  for their technical assistance. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creative commons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Nanoparticle Tracking Analysis (NTA) Vesicles were analyzed using a NanoSight LM10 instrument (NanoSight, Salisbury, UK). For this analysis, a monochromati laser beam at 405 nm was applied to the dilute suspension of vesicles. A video of 60 s duration was taken with a frame rate of 30 frames/s, and particle movement was analyzed using NTA software (version 2. 1, NanoSight, Malvern Instruments Ltd. , Malvern, UK). The NTA software is able to identify and track individual nanoparticles moving under Brownian motion and relates the velocity of particle movement to a particle size by applying the two dimensional Stokes-Einstein equation. The range of sizes that can be analyzed by NTA depends on the particle type, a high refractive index (e. g. , colloidal gold) or a low refractive index (e. g. , cell-derived vesicles).
Entrapment Efficiency of Curcumin The percentage of drug incorporated was determined by centrifuging the drug-loaded nanoliposomes at 9000× g for 15 min to separate the unloaded curcumin crystals from the liposome. After centrifugation, an aliquot of liposomal dispersion was dissolved in 2 mL of methanol and the concentration of curcumin was assayed by HPLC at 425 nm after filtration through a membrane filter (0. 2 µm) (Minisart ® RC15 Syringe Filters, Sartorius, Germany). The concentration of curcumin was determined by a reverse-phase HPLC system (Shimadzu, Kyoto, Japan) equipped with a quaternary pump (LC-20AD), an auto-injector (SIL-20AC HT), a UV-Vis photodiode array detector (UV-Vis PDA, SPD-M20A, Shimadzu, Marne-la-Vallée, France), a Zorbex SB-C18 column (5 µm, 4. 6 mm × 250 mm) and Labsolution data software (Shimadzu, Marne-la-Vallée, France).
The fluorescent probe was vertically and horizontally oriented in the lipid bilayer. The fluorescent intensity of the samples was measured with Tescan INFINITE 200R PRO (Austria) equipped with fluorescent polarizers. Samples were excited at 360 nm and emission was recorded at 430 nm under constant stirring at 25 • C. The Magellan 7 software (Tecan group Ltd. , Männedorf, Switzerland), was used for data analysis. The polarization value (P) of TMA-DPH was calculated using the following equation: P = I II -GI ⊥ I II + 2GI ⊥ , (2) where I II is the fluorescent intensity parallel to the excitation plane, I ⊥ the fluorescent intensity perpendicular to the excitation plane, and G is the factor that accounts for transmission efficiency. Membrane fluidity is defined as 1/P. The results were presented as triplicate analyses.
CSRs are now being promoted as a valuable resource in systematic reviews  [5, 8, 9, 12] , but because data sharing directly via companies and other various platforms is still relatively new, their existence may still be unknown to many systematic review authors, and their utility is still largely unexplored  [24]. The US Agency for Healthcare Research and Quality (AHRQ) has, amongst others, noted the value of searching for 'regulatory documents' as a means of addressing reporting bias  [25]. The term 'regulatory documents' can be used to describe a number of sources of information other than CSRs, including Food and Drug Administration (FDA) approval documents on the Drugs@FDA website (e. g. medical and statistical reviewer reports), European Public Assessment Reports and any document produced by, or held by, a regulatory agency.
To assess readiness and raise awareness, we carried out a preliminary survey to gain insight to the level of understanding, familiarity with and views on the importance of CSRs and other regulatory documents. We also explored what has previously motivated authors to seek data from these sources, and barriers to using them in Cochrane and other systematic reviews. We then carried out a follow-up survey of respondents who had considered or used regulatory data in their systematic review, to explore under what circumstances they thought it most important to seek CSRs as a data source.
Sample selection The release of the initial survey intended for Cochrane review authors was first announced in the Cochrane Digest, which was emailed to all Cochrane authors on 10 June 2016. It was then mentioned again in the Cochrane Digest 2 weeks later. The release intended for authors of non-Cochrane reviews was first advertised on the University of York Centre for Reviews and Dissemination web site on the 25 June 2016 and then on the Systematic Reviews journal web site. Links to this were also shared via social media. The follow-up survey was sent only to those Cochrane respondents who had previously considered or used regulatory data in their systematic review and who had agreed during the first survey to participate in the follow-up. Although several authors of this manuscript have previously used data from regulatory documents, purposely none participated in either survey.
Data analysis Descriptive statistics were used to express quantitative responses including number(s), frequencies and percentages. Verbatim responses were discussed within the team and then tabulated. Responses were generally short and wide-ranging and coded into categories by two research team members (AH, KCD). Since the two first survey questionnaires intended for (a) Cochrane and (b) other systematic review authors were the same, and because some respondents answered the Cochrane questionnaire based on non-Cochrane reviews and vice versa, and as there were few responses to the non-Cochrane version, we combined and analysed responses to both together (Fig. 1 ). We firstly checked that there was no duplication or double counting of reviews. We obtained publications for the systematic reviews to which respondents referred (if provided by the respondents in the survey).
Only two requests were made to the data sharing websites, Clinical Study Data Request (CSDR) and YODA, where each was successful in obtaining the data. Amongst the 20 respondents who requested regulatory data (Table  2 ), 16 had obtained and used the data in their review, two had not yet received the data and their review was ongoing at the time of completing the survey. One respondent said they had received only baseline data and therefore did not include it in their review, and one reported being unable to access the full data because the study was stopped early due to reports of unexpected side-effects. Clinical study reports were acquired by 12 (60%) of the respondents. Five obtained Medical and Statistical Reviews from the FDA, two obtained European Public Assessment Reports (EPARs), and one used other regulatory material including a protocol, case report forms and adverse reaction reports.
Clinical study reports were acquired by 12/20 (60%) of the respondents requesting data, but other regulatory documents including Medical and Statistical Reviews from the FDA (5/20 (25%)), European Public Assessment Reports (EPARs) (2/20 (10%)), and protocols, case report forms and post-marketing adverse reaction reports were also obtained. For the respondents who obtained CSRs, 9/12 (75%) had used the data in their review, in order to include unpublished trials in their meta-analyses and to supplement published data. Two of the respondents were still waiting for the data, one respondent noted that the pharmaceutical company could not provide the data because the study for which the request was made was stopped early due to reports of unexpected side-effects and another respondent reported that only baseline data were provided.
Accessing regulatory data Based on interactions with the community, it seems that many researchers undertaking systematic reviews are still unaware of the various data sharing platforms that provide access to regulatory trial documents and datasets. This may be partly because such data sharing platforms are relatively new and evolving. Furthermore, the limited formal guidance available to systematic review authors explaining how to identify and access regulatory data might also explain why they are rarely used or considered. For example, as noted above, the current version of the Cochrane Handbook does not currently discuss regulatory docum e n t sa n dt h ed a t at h a tt h e s em i g h tc o n t a i n ,w h e r e to find these data, or how to include them  [31]. In our study, participants were asked what could be done to promote and support greater use of CSRs and other regulatory documents.
2 µl of the first-round PCR products, using the same conditions described above. All PCR reactions included negative controls. PCR products were then analyzed on 1. 5% agarose gels and stained with SYBR-safe (Invitrogen). Sequencing was  subsequently performed on all gel-purified PCR products (QIAquick extraction kit, Qiagen). Bi-allelic direct DNA sequencing was applied using BigDye Terminator v1. 1 reagent (Applied Biosystems, USA), on an ABI 3730 automated genetic analyzer and analyzed using the 4peaks software. Sequences were then compared with those present in the NCBI public databases using BLAST. The presence of Yersinia was assumed if BLAST returned a homology score with Yersinia of at least 95%.
Validation of the PCR Methods To evaluate the sensitivity of the PCR methods, we serially diluted the DNA samples between 1 to 10,000 genomes based on an estimated genome weight of ∼4. 7 × 10 -6 ng. The serial DNA dilutions underwent 35 PCR cycles, but no PCR products were observed in the majority of cases (Figure  1C ). We thus performed a second round of 35 cycles, which allowed us to detect the bacteria. In these conditions, the thresholds were estimated between 1 and 100 equivalent genomes. We also determined if the PCR techniques were efficient in detecting Yersinia in a DNA sample containing human genomic DNA. The presence of eukaryotic DNA did not alter the sensitivity of the methods (data not shown). We also confirmed that genomic pre-amplification did not significantly change the capacity of the PCR to detect Yersinia DNA (data not shown).
Yersinia in CD and Controls We analyzed a total of 140 samples from 76 controls (ischemic colitis, intestinal obstructions, diverticular diseases and others) (Table  3 ). Seventy nine samples were from cohort 1 and consisted of mucosal tissues and lymph nodes. We found 6/54 specimens and 3/25 lymph nodes positive for Yersinia corresponding to a   211 ) total of 11%. When several samples were available for the same individual, some were found positive, while others were not. This result indicated that the status of a given individual was highly dependent on the number of samples studied but also on the number of PCR reactions conducted on the same DNA sample (data not shown). In cohort 2, we found Yersinia DNA in 2/26 Peyer's patches (8%) and 0/35 mucosal biopsies, corresponding to a total of 3%.
ACKNOWLEDGMENTS This work has been supported by  Investissements d'Avenir programme   ANR-11-IDEX-0005-02 ,  Sorbonne Paris Cite, Laboratoire d'excellence INFLAMEX. We thank all investigators (researchers, clinicians and surgeons) of the REMIND group who collected the ileal samples. We thank  Rida Zouak  for technical assistance and  Elisabeth Carniel  who gave us the reference Yersinia strains.
Linear regression analysis showed that there is a linear positive regression relationship between measurements carried out on different days (r 2 ¼ 0. 9981), which was highly significant ( p-value , 0. 0001), as calculated using the Prism v5. 03 software. Therefore, we show that our methodology has high repeatability. The diffusion curves of the peptide (figure  2  and electronic supplementary material, figure  S3 ) indicate that the magnitude of GO density in the membrane has a major effect on the rate of mass transport. Depositing increasing densities/quantities of GO in the membrane leads to a gradual decrease in the time taken for the peptide to reach half of its maximal intensity (t 1/2 ). Therefore, the density of GO in the membrane determines the rate of retardation of mass transport.
The peptide was characterized to have a high HPLC purity (of more than 95%) and a molecular weight measured by mass spectrometry analysis of 2667. 51 Da (electronic supplementary material, data 5). In a separate study, the data from Joshi et al. [14]  show that when a GO membrane is immersed in an ionic solution, hydration increases the GO spacing to approximately 0. 9 nm. Unlike Joshi et al. , we do not use vacuum filtration for the preparation of the GO membrane. However at higher GO membrane densities, when a more homogeneous GO membrane is attained, peptide transport does not occur and so our result is in good agreement with what was found previously by Joshi et al. [14]. The same experiments were then carried out using GO with a smaller average size (approx. 90 nm) (Nano Graphene Oxide Solution, Graphene Supermarket, https://graphenesupermarket.
Using numerical modelling to predict drug release by varying the density of graphene oxide membranes The numerical simulations presented in this study are the first demonstration of the use of commercial finite element software COMSOL Multiphysics (figures 4 and 5) for the determination of the diffusion rate of a therapeutic peptide. Our results show that we are able to clearly reproduce most of the features of the experimental data (figures 2 and 3). In this way, we are now able to predict the outcomes of drug release from GO membranes and GO hydrogels by considering membranes and hydrogels of varying GO densities. We applied an effective model for porous media to obtain the numerical results (figures 4 and 5). The effective and 'free' diffusivities are usually related according to the equation D eff ¼ D1/t, where 1 is the porosity of the structure and t is the tortuosity, which is a measure of the actual length per unit effective length a molecule has to diffuse in a porous structure.
Interestingly, it has been previously shown that a homogenization approach of Fick's equation can be used to control processes of mass diffusion for biological and engineering applications  [36, 37]. To assess the fit of the model to the experimental data, the acquired experimental data were compared with the effective mathematical models. The same equation as in  [36]  is used to retrieve in (figure  5c, d ) the main features of the experiments shown in (figure  3 ). However, one notes that there is a decrease in the concentration after a certain time in (figure  2 ): this effect is the result of photo-thermal degradation of fluorophores. To account for this in the numerical modelling, a certain activation term a eff is added in the diffusion equation (3. 2) as described in the methods, and as a result one gets figure 5a,b, which reproduces the observed experimental effect.
Identification and quantification of phenolic compounds by UPLC-MS analysis Chromatographic separation of analytes was carried out on an Acquity UPLC (Waters, Milford, MA, USA) system equipped with a binary pump, autosampler, photodiode array (PDA) detector, column manager, data station running the Compass acquisition and data software. Compounds were separated on an Acquity BEH, C18 column (100 mm × 2. 1 mm, 1. 7 µm) maintained at 40 °C. The eluent system consisted of solvents A (0. 1% formic acid in ultra pure water) and B (100% acetonitrile) with a linear gradient programmed as follows: 0. 0-14 min, 5% B; 15-17 min, 100% B; 18 min, 5% B. The flow rate was 0. 4 mL/min, temperature of sample 12 °C and sample injection volume 1 µL. The effluents from the PDA detector were introduced directly into the quadrupole-time of flight mass spectrometer (Q-TOF) equipped with an electrospray ionization source controlled by HyStar 3.
2 SR2 software (Bruker Daltonic, Bremen, Germany). All MS data were recorded in ESI negative ionization mode in a range of 80-1200 m/z, the capillary voltage was maintained at +4000 V. Nitrogen was used as a nebulizer gas at 2. 5 bar and drying gas at flow rate of 10 L/min. The peaks were identified by comparing their retention times and parent ions with external standards, references and commercial databases. Selected phenolics were quantified using an Acquity UPLCTM H-Class equipped with Xevo TQ-S tandem quadrupole mass spectrometer (Waters, Milford, MA) operating in negative electrospray ionization (ESI) mode, capillary voltage was set to 1500 V, cone voltage -20 V, source offset -50 V. Desolvation temperature was 450 °C, desolvation gas flow -1000 L/h, cone gas flow -150 L/h and nebulizer gas flow was set to 7 L/h.
Chromatographic separation was performed using the same column and solvents as described above with a linear gradient programmed as: 0. 0-7 min, 5% B; 8-9 min, 50,7% B; 10-11 min, 100% B; 12-20 min, 5% B. The flow rate was 0. 4 mL/min and sample injection volume was 5 µL. MS detection was achieved in the single-ion-monitoring (SIM) mode. The m/z values and dwell times of components were set as follows: 169. 1595m/z and 0. 1 s (gallic acid), 301. 0957 m/z at 0. 025 s (ellagic acid), 447. 1595 m/z at 0. 025 s (quercitrin), 609. 2233 m/z at 0. 025 s (rutin), 951. 1957 m/z at 0. 050 s (geraniin), 991. 1000 m/z at 0. 025 s (phyllanthusiin D), 1109. 1000 m/z at 0. 050 s (elaeocarpusin). MassLynx 4. 1 software was used for instrument control and data collection. All samples were run in triplicates.
MATERIALS AND METHODS An analytical workflow was developed, which is composed of the three following steps: structural identifiability, optimal design and evaluation of the optimized design performance. R version 3. 3. 1 was used for calculations, data management and visualizations  (18). For optimal design procedures of step 2 of the analytical workflow, the PFIM program 4. 0 running in R was used  (19). PBPK simulations were performed with Simcyp software (Simcyp, Sheffield, UK) V15. R1 to derive PBPK model parameters value for optimal design procedures in step 2 and the stochastic simulations and estimations in step 3. Stochastic simulations and estimations were performed in step 3 using NONMEM version 7. 3  (20)  and Perl-speaks-NONMEM software package version 4. 6. 0 (21).
17  (23). In the dispersion model, CLp values can be directly derived from PK data of intravenously administered drugs. In the paediatric population, the unbound drug fraction in plasma (fu) and the blood to plasma ratio (B:P) of a drug can be obtained experimentally and, assuming these values were obtained precisely and accurately, they were fixed in the model (equation 1-6). Qh and CLint ,u,WL are thereafter the only two parameters that remain to be estimated. As in this case the dispersion model can be written as a single equation with two unknowns, clinical data on the PK of one drug will not yield structural identifiability. Therefore clinical trial scenarios were explored based on obtaining PK profiles of two different drugs, in which case there would be two equations for clearance, each with two unknowns.
Hypothetical Drug Combinations A total of 99 hypothetical drugs were generated and each unique combination was tested in the design. In order to reduce computational costs and increase interpretability of the results, fu and B:P were taken to be 1. Using the dispersion model, population values of CLint u,WL (Θ CLint u;WL ) were calculated to be such that ER values ranged from 0. 01 to 0. 99. This required population values of Qh (Θ Qh ) which was obtained by simulating 500 male and 500 female individuals of 1 year in Simcyp and estimating the population parameter value using the Bfitdist^function from the fitdistrplus R package  (24)  in R.
Studied Models and Parameters For illustration, the proposed workflow is applied to identifying Qh and CLint ,u,WL in the paediatric population. As none of the parameters in the dispersion model are impacted by absorption or distribution processes, it is possible to study this PBPK sub-model in full while simplifying the remaining aspects of the PK model for the purpose of this work. Therefore, for each drug in the design (drug A and B), the structural model was a one compartment PK model with a volume of distribution VA and VB respectively and with constant intravenous infusion (see PFIM model code in supplementary material 2 and corresponding model equations in supplementary material 1). Clearance was parameterized as defined by the dispersion model (equation 1-6). CLint u,WL for drug A and B was parameterized according to equations 8 and 9 respectively.