dataesr/scientific-paragraphs-categorization huggingface dataset:
The d fferential induction and in situ expression data suggested that the first part of the pathway (possibly up to 7-deoxyloganic acid) corresponds to one transcriptional regulon, whereas the subsequent steps up to the synthesis of secologanin would comprise a second regulon. On the basis of the hypothetical pathway and predicted enzyme activities catalysing the hitherto missing steps (Fig. 1 ), we screened our data set for genes encoding NAD(P)-binding Rossmann fold domain-type oxidoreductases, cytochrome P450 monooxygenases (P450s) and UDP-glycosyltransferases (UGTs) that display co-expression with GES/G8O, and for P450s that show co-expression with LAMT/SLS. Three genes encoding putative oxidoreductases showed a high degree of co-expression with GES/G8O (Fig. 2a ). The first (accession number Caros008267 in the ORCAE database: http://bioinformatics.
Transcriptomic analysis. Transgenic derivatives of C. roseus cell line MP183L (overexpressing the ORCA transcription factors) were generated by particle bombardment  31  with derivatives of the pER8 plasmid  32  carrying either the ORCA2 or ORCA3 open reading frames (ORFs). Selected transgenic lines were treated for 24 h with 10 mM estradiol, and RNA was isolated as described  16. Illumina HiSeq2000 RNA sequencing, assembly, annotation and mapping of the RNA-Seq reads on the reference transcriptome was carried out as described  16. Complete linkage hierarchical cluster analysis was achieved using the CLUSTER and TREEVIEW software  33  and the log 10 transformed values of the normalized FPKM values were used as input for CLUSTER. Proteomic analysis. For the plant material of C. roseus var. little Bright Eyes seeds were sown in sterilized soil and covered with transparent plastic until germination.
For protein identification, databases were searched using Mascot v2. 3. Raw data were searched against a composite database consisting of all entries in the NCBI Viridiplantae database (released in November 2010), all publicly available C. roseus-expressed sequence tags (downloaded from NCBI in November 2010) and the reference transcriptome released on CathaCyc and ORCAE  16  (database contained forward and reverse protein entries, total number of protein entries 1,166,013). The parameters for precursor and fragment ion mass tolerance were set to 5 p. p. m. and 0. 8 Da, respectively. One missed trypsin cleavage was allowed. Carboxyamidomethylation of cysteine was specified as fixed modification, and oxidation of methionine and pyroglutamate formation from glutamine were selected as variable modifications.
Scaffold v3. 0 (Proteome Software, Portland, OR, USA) was used to validate and quantify MS/MS-based peptide and protein identifications. Peptide identifications were accepted if they were established at 495% probability as specified by the Peptide Prophet algorithm  38. Protein identifications were accepted if they were established at 490% probability and at least one peptide was uniquely assigned to a corresponding protein in a minimum of two of our samples. Protein probability was assigned by the Protein Prophet algorithm  39. Proteins that were identified with the same set of peptides and could not be differentiated by the MS/MS analysis were grouped to protein clusters to satisfy the principles of parsimony. Peptide and protein false-discovery rates (FDR) were determined by the Scaffold software.
A peptide FDR of 0. 01% and a protein FDR of 0. 2% were computed. The Scaffold software was also used to determine protein abundance in the mesophyll and epidermal factions based on the number of spectra assigned to each protein. An F-test was applied to assess significant differences in protein abundances. Gene isolation. ORFs were amplified by PCR from a pACT2 cDNA library of a C. roseus cell culture elicited with yeast extract  40  using the primers listed in Supplementary Table  4. For expression in plants, the ORFs were transferred to vector pRT101 (ref. 41) to bring them under the control of the Cauliflower Mosaic Virus 35S promoter, and the expression cassettes were then transferred to the binary vector pCAMBIA1300 (Cambia). For expression in E. coli, the ORFs were transferred to vector pASK-IBA45plus (IBA) and/or pET16-H (Novagen pET-16b derivative).
In several experiments, pathway intermediates were injected into the same leaves 3 days after agroinfiltration. Compounds used for infiltration were diluted to a final concentration of 400 mM in methanol/water (1:19), with the same ratio of methanol/water alone as a negative control and 400 mM 7-deoxyloganic acid, 8-carboxygeranic acid, secologanin or strictosidine as positive controls. Leaves were harvested for metabolite analysis 5 days after agroinfiltration. Frozen, powdered N. benthamiana leaves (200 mg aliquots) were extracted in 0. 6 ml 99. 867% methanol, 0. 133% formic acid and 5 ml of the extract was analysed using a Waters Alliance 2795 HPLC connected to a QTOF Ultima V4. 00. 00 mass spectrometer (Waters, MS Technologies, UK). Measurements were taken in negative ionization mode. LC-MS data were acquired using MassLynx 4.
Acknowledgements We are grateful to  Richard Twyman  for critical reading of the manuscript and to  Søren Rosendal Jensen  for advice on iridoid substrate synthesis. David Nelson  is acknowledged for naming P450 enzymes. The research leading to these results has received funding from the  European Union Seventh Framework Programme   FP7/2007-2013  under grant agreement number  222716-SMARTCELL. T. I. received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  7th Framework Programme  ( FP7/2007-2013) under REA Grant Agreement 289217.
Experimental Groups We extracted and re-analysed data from two experimental groups that were previously published  [7]. In this article, the liver concentrations of BOPTA were measured but hepatocyte and bile canalicular concentrations were not extracted. The two groups of rat livers were perfused with the same protocol: (1) Livers isolated from normal rats and perfused with 200 µM gadobenate dimeglumine (BOPTA, MultiHance ® , Bracco Diagnostics, Milan, Italy) (BOPTA group, n = 5); and (2) livers isolated from rats deficient in Mrp2 and perfused with 200 µM BOPTA (BOPTA-TR group, n = 3). A third group of rat livers was added. In this group, 64 µM mebrofenin (MEB, Bridatec ® , GE Healthcare, Chalfont St. Giles, UK) was perfused (MEB group, n = 5). Male Sprague-Dawley rats (Charles River, Les Arbreles, France) were anesthetized with pentobarbital (50 mg/kg, intraperitoneal injection).
Acknowledgements H. R. and D. W. are grateful to the  Agence Nationale de la Recherche  for the funding of the  ANR-10-BLAN-1528   PHENOWALL  project, and to the  University of Strasbourg Institute for Advanced Study (USIAS)  and the  Freiburg Institute for Advanced Studies (FRIAS)  for the funding of the  METABEVO  project. D. W. in addition acknowledges the support of the  European Community 's  Framework VII Program   FP7/2007-2013  to the  SMARTCELL  project  KBBE-2007-3-1-01. B. R. H. and J. -E. B. wish to acknowledge financial support provided by the  UNIK [Universitetsforskningens Investeringskapital (Investment Capital for University Research)] Center for Synthetic Biology  'bioSYNergy', funded by the  Research Initiative of the Danish Ministry of Science, Technology and Innovation.
Background: Adverse drug reactions and adverse drug events (ADEs) are major public health issues. Many different prospective tools for the automated detection of ADEs in hospital databases have been developed and evaluated. The objective of the present study was to evaluate an automated method for the retrospective detection of ADEs with hyperkalaemia during inpatient stays. Methods: We used a set of complex detection rules to take account of the patient's clinical and biological context and the chronological relationship between the causes and the expected outcome. The dataset consisted of 3,444 inpatient stays in a French general hospital. An automated review was performed for all data and the results were compared with those of an expert chart review. The complex detection rules' analytical quality was evaluated for ADEs.
Detection of ADEs Retrospective detection of ADEs In post-marketing (phase IV) studies, pharmacovigilance data are typically obtained from spontaneous reports by individual health professionals faced with an anomaly that they consider to be an ADR. These reports are not exhaustive and are known to suffer from underreporting bias  [9, 10]. One can therefore reasonably consider that conventional pharmacovigilance data (i) does not encompass all ADRs and (ii) provides little information on ADEs. Moreover, these declarative data have already been interpreted and do not contain any control cases. These limitations increase the potential value of gathering objective data, such as electronic medical records from hospital information systems. In general, the data in these systems (drug administrations, laboratory results and administrative data) have been routinely and exhaustively collected and are very appropriate for retrospective cohort studies using alternative signal discovery approaches, such as data mining  [11] [12] [13] [14] [15].
Electronic medical records constitute a major resource for observational, post-marketing analyses. Given that ADEs are rare, it is necessary to enhance the power of statistical analyses by building inter-hospital databases. The European Union's PSIP research project  [16]  (which encompasses this study) has enabled the construction of this type of database. Furthermore, the PSIP project has prompted the development of a custom common data model  [17] , a method for solving the semantic interoperability issues that affect laboratory results databases  [18] , and a method for free-text automated deidentification  [19].
Inpatient stays used for the study The dataset comprised inpatient stays (defined here as at least two consecutive overnight hospitalizations) in a French general hospital during the first nine months of 2010. All the data were obtained from the electronic medical records stored in the hospital's health information system (sourced in the departments of geriatrics, internal medicine, pulmonology, cardiology, gastroenterology and surgery). The number of inpatient stays (with a length of stay greater than two days) in the database over this period is 3,444. The de-identification of the medical records was performed using FASDIM algorithm  [19]. The ethical agreement to analyze the medical records was given by the French Commission Nationale Informatique et Libertés.
Renal failure |fflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflffl} An inpatient stay was considered to be positive for a "complex detection rule" when all three conditions ("cause conditions" AND "outcome" AND "consistent chronology") were met. It should be borne in mind that these rules were only applied retrospectively, since an outcome is always present. The presence of "cause conditions" means that a drug and a context favouring the hyperkalaemia must both be present in the five days prior to the outcome. After the set of rules had been elaborated by the expert pharmacists and pharmacologists, it was optimized by testing against a dataset of previous inpatient stays.
Automated detection of ADEs in inpatient stays A set of scripts programmed in R  [40]  was used to scan the hospital database with the set of detection rules. The scripts work automatically with data that comply with the common model developed in the PSIP project and generate XML files as their output. A web-based "ADE Scorecards" tool  [41]  enables the expert to review the automatically detected cases and the corresponding, full electronic medical record.
The a priori system favours recall over precision so as not to miss an ADE. In this context, the harmonic mean (the F-measure) is not a relevant criterion. It is important to note that (i) any stay that triggers a rule has at least one hyperkalaemia event (since the latter is the rule's outcome) and (ii) any stay selected by the expert also necessarily features at least one hyperkalaemia event (since it is the focus of the present study). Thus, the contingency table can be completed by reviewing only inpatient stays that feature a hyperkalaemia event. This was undertaken in the analysis -especially for the expert review. It should be noted that the ability to detect hyperkalaemia per se does not generally need to be assessed; it is an objective element obtained by simply querying the laboratory results database.
Discussion In the present study, we used a set of complex detection rules to retrospectively detect ADEs with hyperkalaemia in a hospital database. This automated detection step yielded high recall and precision values, relative to expert chart review. In terms of the recall, 89. 5% of ADEs with hyperkalaemia (regardless of the presence or absence of an abnormal symptom). As a result, all the serious ADEs were automatically detected: this is an important result, even though the small number of cases prevents us from extrapolating this finding to other situations. In terms of precision, 63. 7% of the automatically identified ADEs with hyperkalaemia were true ADEs. Our present results appear to confirm the need to take account of a patient's clinical and biological context when seeking to automatically detect particular ADEs.
Again, the rules were laboratory alerts (for the "potassium" trigger) and the ADEs were validated  Consequently, our present results appear to be more satisfactory that those reported in the three abovementioned studies. However, we cannot be sure that the same quality of results would be obtained with a set of detection rules for another outcome (i. e. another type of ADE). Furthermore, the low number of ADEs in this study means that it is difficult to generalize these results: ADEs are rare and so the construction of inter-hospital databases may provide more robust results. These retrospective, complex detection rules might be of value in two situations. Firstly, expert chart review is a time-consuming task. Automated detection might enable a physician or pharmacologist to perform a more focused review of a few automatically detected stays.
This approach would reduce the number of inpatient stays to be reviewed. This step can be envisaged because of our technique's high recall. The tool presented here could also be used to generate retrospective alerts during hospitalization. A pharmacovigilance specialist could then review the alert and, if a drug is suspected to be responsible for an ADE, contact the patient's attending physician  [44]. Secondly, the performance of our set of complex detection rules shows that automated techniques could be used to estimate the occurrence of ADEs. If a technique's precision and recall are constant, then the number of ADEs could be estimated as "n automated detections × * (Precision/Recall)". This type of computerized tool may therefore have a role in hospital pharmacoepidemiology through the routine analysis of large, inter-hospital databases.
Conclusions The objective of the present study was to create and then evaluate complex detection rules that take account of the clinical and biological context in which a drug is prescribed and the chronological relationship between the cause and the expected outcome. The complex detection rules were evaluated against a nine-month database set of inpatient stays at a French general hospital. The method's estimated recall and precision appear to be clinically relevant. A system based on these rules is now in routine use by the general hospital's physicians and pharmacists and is being deployed in other hospitals in the region. We consider that this type of automated tool could have a role in increasing patient safety and quality of care.
Pharmacokinetic data analysis Pharmacokinetics and PK/PD modelling were performed by the least-squares regression method, using commercial software (WinNonlin version 5. 2, Pharsight Corporation, Mountain View, CA, USA). Goodness of fit and selection of the appropriate model were evaluated using the Akaike Information Criterion estimate  (Yamaoka et al. , 1978)  and by visual inspection of the fitted curves and residuals. Blood robenacoxib and plasma ketoprofen enantiomer concentrations C(t) were fitted for each cat using an equation corresponding to drug disposition in a two-compartmental model with absorption phase (subcutaneous administration, Eq. 1): ). (  2 ). (  1 ). (  2 1 2 1. ). ( ) ( ag l ag l ag a t t t t tl t k e Y e Y e Y Y t C              (Eq. 1) where λ1 and λ2 are the initial and terminal slopes (/h), Y1 and Y2 the intercepts on the Y axis (ng/mL), when C(t) is plotted on a semi-logarithmic scale, ka is the first-order absorption rate constant (/h) and tlag the absorption lag time after subcutaneous administration.
Data were weighted by the reciprocal of the estimated value for blood or plasma concentration when necessary. Exudate concentrations of robenacoxib or ketoprofen enantiomers Ce(t) were fitted for the data from each cat using an equation corresponding to drug disposition in a bicompartmental model with an absorption phase after dose normalisation (Eq. 2):. ). ( ) (           (Eq. 2) where λe1 and λe2 are the initial and terminal slopes (/h), Ye1 and Ye2 the intercepts on the Y axis (ng/mL) when Ce(t) is plotted on a semi-logarithmic scale, and kea is the first-order invasion rate constant in exudate (/h). No weighting was applied to the data for fitting. It was assumed that only a negligible amount of each NSAID gained access to the tissue cage and that the pharmacokinetics in exudate had no effect on the time-course of drug disposition in the rest of the body.
Pharmacodynamic data analysis and PK/PD modelling A user program was purposely written in WinNonlin for PK/PD modelling. The equations of robenacoxib and S(+) ketoprofen enantiomer disposition in blood/plasma C(t) or in exudate Ce(t) were obtained by compartmental pharmacokinetic analysis by fitting equations (1) or (2), respectively, to the observed data. Individual pharmacokinetic parameters were entered as constants to solve the PK/PD models in a 2 stages analysis  (Giraudel et al. , 2005a). In vivo generation of exudate PGE2 was used as a surrogate for COX-2 activity in order to carry out PK/PD modelling of the NSAIDs in exudate  (Lees et al. , 2004). An indirect response model described by  Pelligand et al. (2012b)  was used to model the effect of robenacoxib and S(+) ketoprofen on exudate PGE2 production.
6 (NSAID) in the general Eq. 3, the time development of PGE2 concentration in exudate was described by Eq. 7 (placebo) and Eq. 8 (NSAID): 2 out PLACEBO in 2 out in 2 PGE K stimul K PGE K ) t ( K dt dPGE        (Eq. 7) 2 out n e n 50 n e NSAIDs in 2 PGE. K ) t ( C IC ) t ( C 1 stimul K dt dPGE             (Eq. 8) The time-courses of exudate PGE2 were modelled simultaneously for placebo and robenacoxib, then placebo and S(+) ketoprofen, as the equations for placebo and NSAIDs share several common parameters in the same cat (Kin, Kout and carrag, k1 and k2). Nine parameters were estimated by the model, namely kin, carrag, k1, k2, tlag1, tlag2, kout IC50 and n. Ex vivo generation of serum TxB2 was used as a surrogate marker of COX-1 activity for PK/PD modelling. The NSAID concentration in the central compartment produced an inhibition of serum TxB2 synthesis according to the following sigmoid Imax model selected to fit the serum TxB2 data (Eq.
ACKNOWLEDGEMENTS This study was supported by  Novartis Animal Health, Inc and the Biotechnology and Biological Sciences Research Council UK (Industrial CASE Award)  [grant number:  BB/E528544/1 ] awarded to L. P. We thank  Birgit Ranheim  for initiating the collaboration with the  Oslo School of Veterinary Science. DECARATIONS OF INTEREST  L. Pelligand received a  CASE award  from  BBSRC  and  Novartis Animal Health   J. N. King  is an employee of  Novartis Animal Health P. Lees has acted as a consultant to  Novartis Animal Health.
In some cases, the selectable marker and metabolic function could be conveyed by the same gene. These results also point to a potential complex interplay of the metabolism of herbicides with that of specialized plant compounds, and to a possible effect of herbicide treatment and Table  II. Catalytic parameters of phenylurea metabolism by A. thaliana CYP76C enzymes Kinetic assays were carried out in a final volume of 200 mL for 20 min in the presence of 1 mM NADPH, 7 pmol of P450, and variable substrate concentrations. Kinetic parameters were deduced from Michaelis-Menten representation. dM-CTU: monodemethyl-chlorotoluron; dM-IPU: monodemethyl-isoproturon; OH-CTU: ring-hydroxymethyl-chlorotuluron; OH-IPU: hydroxyisopropyl-isoproturon. Data are means 6 SD of three determinations. Units for catalytic parameters are as follows: K m (mM), kcat (min 21 ), and kcat /K m (mM 21 min 21 ).
CYP76C Subfamily History and Phylogeny CYP76 coding sequences from various species were retrieved from Phytozome (http://www. phytozome. org) and GenBank (https://www. ncbi. nlm. nih. gov/ genbank/) databases. Coding sequences were translated into amino acid sequences and aligned with MUSCLE  (Edgar, 2004)  prior to determination of Gblocks  (Castresana, 2000)  using the SeaView software (http://pbil. univ-lyon1. fr/;  Gouy et al. , 2010). The corresponding nucleotides Gblocks alignment (Supplemental Data Set S1) was subsequently used for phylogeny reconstruction by maximum likelihood analysis with PhyML 3. 0  (Guindon et al. , 2010)  using the generalized time reversible model (default settings except that the proportion of invariable sites was estimated). Phylogeny consistency was tested by performing 100 bootstrap iterations.
The output tree was shaped using the FigTree software (http://tree. bio. ed. ac. uk/software/figtree/). Organization of CYP76C genes in the Arabidopsis thaliana genome was realized according to the chromosome map tool from The Arabidopsis Information Resource (http://www. arabidopsis. org/jsp/ChromosomeMap/tool. jsp). Synteny analysis of the CYP76C3 and CYP76C7 loci was realized with the help of the synteny tool on the Phytozome Web site (http://www. phytozome. net/).
Quantification of Gene Expression Quantification of gene expression was carried out by qRT-PCR as previously described  (Ginglinger et al. , 2013). The different organs of A. thaliana and A. lyrata were harvested from five different plants at the flowering stage and were immediately frozen in liquid nitrogen. For the normalization of gene expression in A. lyrata, the orthologs of A. thaliana SAND-like (gene 481666) and TIP41-like (gene 491240) were used after their stable expression was validated among four putative reference genes by the GeNorm  (Vandesompele et al. , 2002)  and NormFinder  (Andersen et al. , 2004)  algorithms and GenEx 4 software (http://genex. gene-quantification. info/). Oligonucleotides used for each gene are provided in Supplemental Table  S3. Relative expression was calculated with the specific efficiency of each primer pair using the EDCt method  (Pfaffl, 2001).
Sequence data from this article can be found in the Arabidopsis Genome Initiative, GenBank/EMBL, or Phytozome databases under the following accession numbers: At-SAND-like (At2g28390), Al-SAND-like (gene 481666), At-TIP41-like (At4g34270), Al-TIP41-like (491240), At-PP2AA2 (At1g13320), Al-PP2AA2 (gene 936261), At-EXP (At4g26410), At-TUB4 (At5g44340), Al-ACT2 (gene 342019), At-CYP75B1 (At5g07990); At-CYP76C1 (At2g45560), Al-CYP76C1p (gene 871078), At-CYP76C2 (At2g45570), Al-CYP76C2 (gene 346366), At-CYP76C3 (At2g45580), Al-CYP76C3 (gene 322211), At-CYP76C4 (At2g45550), At-CYP76C5 (At1g33730), At-CYP76C6 (At1g33720), At-CYP76C7 (At3g61040), Al-CYP76C7 (gene 486570); At-CYP76C8p (At3g61035), Al-CYP76C8 (gene 867547), At-CYP76G1 (At3g52970), Al-CYP76G1 (gene 348388), Cr-CYP76B6 (AJ251269), and Ht-CYP76B1 (Y10098).