dataesr/scientific-paragraphs-categorization huggingface dataset:
Fig. 2. Effects of fipronil on the rat hepatic transcriptome. (A) Gene expression profiles were obtained by microarray analysis (Agilent 4x44K whole rat genome) of liver samples from rats treated with fipronil (3 mg/kg per day, 14 days, n = 8) or the vehicle alone (n = 7). The heatmap represents the data obtained on all samples for the 712 differentially expressed probes (402 up-and 310 downregulated, q-value < 0. 1). Hierarchical clustering was applied to the samples and the probes using 1-Pearson correlation coefficient as distance and the Ward's criterion for agglomeration. The top three GO biological processes most significantly enriched among the upregulated or downregulated genes are indicated. (B) Raw microarray data for female rats exposed to the PXR activator pregnenolone-16alpha-carbonitrile (PCN), to the CAR and PXR activator phenobarbital (PB) or to the vehicle alone for 6 h, 24 h or 5 days were obtained from GEO [GSE14712,  [19] ].
Acknowledgments The authors thank  Sylvie Puel  for performing the HPLC assays,  Colette Be  ´toulieres and  Caroline Sommer  for animal breeding,  Nabila Moussaoui  for her help during in vivo experiments and  Didier Concordet  for his advices on statistical analysis. We are grateful to  Pr David D. Moore  and  Pr Steven A. Kliewer  for the CARand PXR-deficient mice. We thank  Pr Urs A. Meyer  for the mouse lines and for constructive discussions. This work was supported by grants from the  Region Midi-Pyre  ´nees and from the  French Ministry of Ecology and Sustainable Development  ( Programme National de Recherche sur les Perturbateurs Endocriniens ,  0000442 ). Beatrice Roques was funded by a grant from the  Ministere de l'Enseignement Superieur et de la Recherche.
METHODOLOGY A descriptive review of national, regional or local health authority, health insurance company or physician association activities across Europe regarding dabigatran up to and including the beginning of 2013 was conducted by one of the co-authors (Brian B. Godman). This was undertaken by collating and appraising relevant published papers and internal documents known to the co-authors as well as any pertinent documents available on the internet. Direct feedback was provided by the co-authors where there was limited or no data available in a particular country. The information provided by the co-authors was subsequently re-checked (Brian B. Godman) to enhance its accuracy. In total, information was collected from over 30 European countries and regions. We have used this methodological approach in previous publications involving health authority and health insurance company personnel when there has been a paucity of published data  (Cheema et al.
g. , only reimbursed in patients already on warfarin if they are unstable with TTR < 65 • Patients have to be followed in a tertiary or secondary care anticoagulation center. Patients can be followed in primary care but only if authorized by the tertiary or secondary care center. • Every patient has to be registered in a database and followed by the IT anticoagulation program • Anticoagulation centers have to report once yearly to the tertiary center regarding the number of patients experiencing minor and major bleeding, thromboembolic events, as well as any deaths from bleeding or thromboembolism with dabigatran Table  2  | Key issues for health authorities and health insurance companies to consider when appraising risk sharing arrangements proposed by pharmaceutical companies for new drugs.
This will facilitate the development of user friendly screens and data entry to enhance the completeness and accuracy of data entry. In addition, incorporate systems that help detect errors quickly regarding data entry, e. g. , trigger tools 6, 2011 that there had already been 256 spontaneous reports of serious bleeding resulting in deaths in the EudraVigilance database  (EMA, 2011). Table  1  and Table  A1  in the Appendix document the extensive range of activities initiated across Europe. These include educational activities pre-launch in Stockholm County Council, Sweden, as well as post-launch activities among regions and localities in Germany, Spain, Sweden, and the UK. There were also prescribing restrictions in some countries alongside the development of shared care protocols between ambulatory and hospital care to improve interface management and enhance the subsequent quality of care  (Godman et al.
Post-launch • Use administrative and/or medical databases to compare "real world" patients with those included in Phase III RCTs in terms of their clinical features, treatments, and potential outcomes to further refine prescribing guidance and/or reimbursed prices especially if greater co-morbidity in "real world" patients  (Joppi et al. , 2013)  • Build in regular reviews of any reimbursement/funding/guidance especially as more data becomes available, e. g. , more recent data challenging "no patient monitoring" with dabigatran especially if "no patient monitoring" was built into submitted economic analyses • Work with health authorities and health insurance companies pre-launch to critically review new treatments, especially where there are concerns with patient safety, to help enhance their appropriate use at launch and their retention on the market • Provide guidance to health authorities and health insurance companies regarding optimal patient populations that maximize the value of new drugs, as well as potential quality/prescribing indicators • Provide input into discussions on the potential value of pertinent pharmacogenetic tests that may help optimize the use of new drugs post-launch • Help with the development of educational materials for physicians and patients peri-and post-launch including the development of any clinical guidelines based on agreed guidance • Assist with the design of any patient registries prior to launch, and follow this up after launch (Table  3 ).
e. , the prescribing unit will be responsible for the cost of the drug. If however patients are entered into the County Council's quality assessment program, the cost will be borne by the County Council. Decision by the County Council to follow the recommendation of the DTC • Resources for treating patients allocated in the 2011-2012 County Council drug budgets • Communication plan implemented (B) Stockholm County Council• Systematic and long-term involvement of medical and scientific expertise in the development of guidelines and advise to patients and prescribers through the Regional Drugs and Therapeutic Committee (DTC) and clinical pharmacologists• Extensive pre-launch activities with key messages broadcasted both to the public and to prescribers through websites of the DTC as well as the Swedish Medical Journal• Appreciable number of pre-launch meetings and training sessions with all major physician groups around the key issues and concerns with dabigatran as well as its likely place in care• Production of educational folders regarding dabigatran, slide kits, published articles, and data on the Janus website as well as published information for patients • Forecasting the potential budget impact in 2011 and 2012 ahead of launch and monitoring this in practice• Development of a laboratory method to monitor dabigatran in plasma with LC-MS/MS technology, and recommending sampling in the introductory phase to build a knowledge database.
The final DMSO concentration remained under 1. 5% (v/v). Data were analyzed as above. All experiments were performed in triplicate at least, and results are expressed as means Ϯ S. D. Crystallization and Structure Determination of Ligand-bound CYP121-Ligand-bound CYP121 crystals were grown at 6 °C in a few days by the sitting drop method as described previously (purified CYP121 at 230 M and ligand concentration around 1 mM in 100 mM NaMES at pH 5. 0 -5. 5 and 1. 6 -2. 2 M (NH 4 ) 2 SO 4 ). Crystals were cryoprotected in a solution containing 20% glycerol in the crystallization solution and cryocooled in liquid nitrogen. Diffraction data were collected at the European Synchrotron Radiation Facility or at SOLEIL (see Table  1  for details), processed with MOSFLM, and scaled with SCALA from the CCP4 package  (22).
The structures were refined with REFMAC  (23)  and BUSTER5  (24) , using the coordinates of native CYP121 as the input entry (Protein Data Bank entry 1N40)  (25) , and manually corrected using COOT 0. 6  (26). Ligands were built at the end of the refinement processes. The quality of the electron density was compatible with the molecule being completely built for complexes of CYP121 with 3, 8, and 15. In the case of CYP121 bound to 4 and CYP121 bound to 7, two alternate conformations were modeled on the basis of the electron density map. Their respective occupancies were adjusted to 0. 25 (conformation 1) and 0. 75 (conformation 2) for 4 and to 0. 34 (conformation a) and 0. 66 (conformation b) for 7, such that B factor values would be of the same order for each conformation. At the end of the refinement process, one additional run of BUSTER5 was performed after removal of the ligand molecules from the coordinate files, to calculate unbiased 2F o Ϫ F c electron density omit maps.
CYP121 Specificity 2. 50 and 39. 5 ppm, respectively). NMR data were processed and analyzed with Bruker TOPSPIN version 3. 1 software. The conformational analysis was based on the measurement of homonuclear 3 J H␣-H␤ coupling constants on one-dimensional 1 H spectra and heteronuclear 3 J H␣-C␥ and 3 J H␤-CO coupling constants measured on phase-sensitive two-dimensional HMBC experiments. Using the Karplus curves obtained for these three vicinal coupling constants  (27) , the populations of 1 rotamers and stereospecific assignment of H␤ methylenic protons could be obtained unambiguously. In particular, the 3 J H␣-C␥ coupling constant provided direct estimates of the gauche ϩ rotamer population, and 3 J H␤-CO coupling constants allowed H␤ methylenic protons to be assigned stereospecifically. NMR assignments of the compounds are presented in Table  2.
The addition of 1% DMSO to a CYP121 solution slightly diminished the intensity of the Soret band (Ͻ1%) without observable effect on max. Furthermore, binding constants of CYP121 for cYY were similar for final DMSO concentrations of 1 or 2% (19. 4 Ϯ 0. 6 and 21. 6 Ϯ 1. 2 M, respectively). Therefore, the final DMSO concentration was kept under 1% in ligand titration experiments. In addition, for ligands exhibiting no spectral variation upon addition to a solution of CYP121, their effects at a high concentration on the affinity of CYP121 for its substrate cYY were evaluated in inhibition experiments. In these assays, the DMSO concentration remained Յ1. 5%. Data obtained in these two experiments are summarized in Table  3. Very little binding or no binding was observed for compounds comprising one aliphatic side chain, cyclo(L-Tyr-L-Leu) (5), cyclo(L-Tyr-L-Met) (6), and cYA  (7).
Three additional compounds appeared specifically in the presence of CYP121, and each accounted for less than 1% of 15. Crystal Structures of cYY Analog-bound CYP121 Reveal a Common Binding Mode-The crystal structures of CYP121 bound to cYF, cYW, cYA, cY-DOPA, and 15 were determined. Data collection and refinement statistics are summarized in Table  1. Independent of the nature of the molecule bound to CYP121, crystals were grown as previously described and were found to belong to the same crystal form as crystals of native CYP121 used for the atomic resolution of the CYP121 structure  (16). The various structures were solved and refined at high resolution (1. 2-1. 9 Å resolution) such that the quality of the structures is compatible with fine structural analysis. The omit maps calculated from the data showed unambiguous density corresponding to the compound bound to CYP121 (Fig.
4 ). One single molecule was modeled in all cases except for cYW, for which two alternate conformations are observed. Each overall structure of ligand-bound CYP121 is highly similar to that of native CYP121 (Protein Data Bank entry 1N40) with root mean square deviations on equivalent C␣ atoms of 0. 177, 0. 112, 0. 169, 0. 133, and 0. 099 Å for complexes of CYP121 bound to cYF, cYW, cYA, cY-DOPA, and 15, respectively (determined for 381, 359, 374, 354, and 359 equivalent C␣ atoms, respectively). Furthermore, for each structure solved, atoms of the side chains of the residues that constitute the active site superimpose well on the equivalent atoms in native CYP121 (Protein Data Bank entry 1N40). In each structure, the ligand binds at the previously described cYY-binding site (Fig. 5 ). There was thus a common binding mode, similar to that identified for cYY.
* This work was supported by the  Commissariat al'Energie Atomique et aux Energies Alternatives (CEA) ,  CNRS , and grants from the  Region Ile-de-France. The  Service d'Ingenierie Moleculaire des Prote  ´ines is a member of the  Laboratory of Excellence LERMIT. The atomic coordinates and structure factors (codes  4ICT ,  4IQ9 ,  4IQ7 ,  4IPW , and  4IPS ) have been deposited in the Protein Data Bank (http://wwpdb. org/). 1 Both authors contributed equally to this work.
MATERIALS AND METHODS Study design. This pharmacokinetic study was nested into a larger efficacy study conducted in the Mbarara National Referral Hospital (MNRH) antenatal clinic (ANC) in Uganda  (11). Full clinical details for the pregnant women in that trial are reported elsewhere  (11). The trial was registered at ClinicalTrials. gov (NCT00495508), and ethical approval was obtained from the Uganda National Council for Science and Technology (ethics committee), the Mbarara University Institutional Ethics Committee, Mbarara University Faculty of Medicine Research and Ethics Committee, and the "Comité de Protection des Personnes," Iles de France XI, France. Inclusion criteria were residence in the Mbarara Municipality (radius of 15 km from MNRH), an estimated gestation age (EGA) of at least 13 weeks, and P.
The retention times of quinine and the internal standard were 4. 9 min and 6. 1 min, respectively. Excitation and emission wavelengths were 350 and 440 nm, respectively. The recovery was between 76% and 80% within the calibration range of 1 to 10 g/ml. Duplicates of quality control samples were analyzed at three concentrations, 2 g/ml, 6 g/ml, and 8 g/ml. Overall accuracy (bias) and precision (RSD) were less than 5. 0% and 9. 9%, respectively, and the LLOQ was set to 1 g/ml. Both bioanalytical laboratories participate in the WorldWide Antimalarial Resistance Network (WWARN) quality control and assurance proficiency testing program (http://www. wwarn. org/toolkit/qaqc). Pharmacokinetic analysis. Individual plasma concentration-time data were evaluated using a noncompartmental approach with WinNonlin version 5.
The apparent volume of distribution (apparent volume of distribution in the terminal elimination phase [V Z ]/oral bioavailability [F]) and oral clearance (elimination clearance [CL]/F) were computed individually using equations 1 and 2. V z F ϭ DOSE ln 2 t 1⁄2 ϫ AUC (1) CL F ϭ DOSE AUC (2) Patients who did not provide a sufficient number of samples for a full pharmacokinetic evaluation were excluded from the analysis but included in the summary statistics for C max , T max , and T lag if the data allowed. Complete in vivo conversion of artemether into dihydroartemisinin was assumed, and the administered dose of dihydroartemisinin was calculated using the relative difference in molecular weights. Lumefantrine samples were collected frequently for all doses and could therefore capture the accumulation of drug over time.
Residual lumefantrine exposure from the 3 days of dosing could not be accurately subtracted from the lumefantrine exposure of the last dose because of its multicompartment pharmacokinetics and the long terminal elimination half-life. Therefore, the total dose of lumefantrine (i. e. , the sum of the six doses) was used as the input dose together with all observed concentration-time data in the noncompartmental analysis of lumefantrine. Quinine plasma samples taken after the first dose (samples taken up to 8 h after the first dose) were used for analysis, since subsequent samples were too sparse (i. e. , only one trough value per day) to compensate fully for the accumulation of the drug over time. Individual pharmacokinetic parameter estimates for lumefantrine were compared between pregnant women and nonpregnant women using the Mann-Whitney test in STATA v.
Lumefantrine pharmacokinetics was also evaluated in a nonpregnant control group and resulted in no statistical difference in total exposure between the groups. However, the terminal elimination half-life was shorter in pregnant women than in nonpregnant women, which will affect cure rates and postprophylactic effects, particularly in women with little background immunity. Artemether/dihydroartemisinin and quinine exposures were generally low in pregnant women compared to literature data, but more data are needed to evaluate the potential impact of pregnancy on therapeutic responses.
ACKNOWLEDGMENTS We sincerely thank the women for their cooperation in completing this study. We thank the diligent staff from the  Mbarara National Referral Hospital (MNRH) ,  Mbarara, Uganda. This study was an initiative of "Aid for poverty related diseases in developing countries " and was cofinanced by  Médecins Sans Frontières  and the  European Commission. This investigation was part of the  Mahidol-Oxford Tropical Medicine.
Results: In this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge.
The aim is then to characterize each SEP in terms of drug and target properties. This can be addressed as a supervised classification task. Two machine-learning methods are chosen for this task: Decision Trees (DTs) and Inductive Logic Programming (ILP) (Figure  1E ). These two methods provide easily readable results which can then be exploited for understanding SEPs. Decision trees use a single table as input in which each row corresponds to a drug and each column to a drug descriptor. Inductive Logic Programming uses relational descriptors to learn a first-order-logic concept definition from observations. Relational descriptors encoding characteristics of both drugs and their targets are retrieved from our "NetworkDB" integrated database, which is built from several data sources including DrugBank, UniProt, KEGG, and GO.
Chemical space: drugs and their properties The SIDER database contains drug side-effect relationships  [3]. DrugBank is used to collect data such as categories and targets  [11]. The join between SIDER and DrugBank is based on the PubChem Compound identifier given by SIDER and DrugBank. A total of 554 drugs from SIDER are referenced in DrugBank v3. 0. Each drug is described by its category and a set of clusters it belongs to. In fact, various structural representations and associated similarity measures were used to cluster drugs. The first similarity measure is based on SMILES representation. The SMILES codes are converted thanks to Open Babel program into fingerprints which allows linear and ring substructures to be identified  [12]. Then, the structural similarity between two molecular fingerprints is calculated using the Tanimoto measure.
In addition, we calculated three other similarity scores using spherical harmonics representation of molecules. This parametric representation of macromolecular surface was originally proposed and applied by Ritchie and Kemp  [13]  and Cai et al. [14]. The proprietary program HPCC (Harmonic Pharma) supports three variants of the spherical harmonic representation. HPCCgeo uses spherical harmonic coefficients (shape information) to calculate similarity between drugs, HPCCchem is based on chemical properties mapped on the spherical harmonic representation, and HPCCcombo combines shape and chemical information. Ward's method is used to perform four hierarchical clusterings of drugs  [15]. The optimal numbers of clusters is determined by the method of Kelley and al. [16]. Thus, 60 clusters are obtained with Tanimoto, 53 with HPCCgeo, 21 with HPCCchem and 34 with HPCCcombo measures.
Biological space: proteins and their properties Drug targets are extracted from both DrugBank and PDB  [17]. The outer join between PDB and DrugBank (retaining all DrugBank targets) is based on SMILES code identity. Drug targets are associated with their UniProt accession numbers. Thus, 768 targets are collected, representing an average of four targets per drug. Then, target annotations are retrieved from different databases. Protein-protein interactions are retrieved from the IntAct database  [18]  and 5959 interactions were collected which correspond to 2827 new proteins. For all the proteins (drug targets and their interactants), 1403 pathway names are extracted from the KEGG database and the Pathway Interaction Database which integrates data from NCI-Nature, BioCarta and Reactome  [19, 20].
Grouping side-effect terms into term clusters Side effects are extracted from SIDER. As shown previously  [23] , the use of all terms describing side effects in SIDER (about 1500) impairs the execution of data mining programs and produces numerous and redundant patterns which are inappropriate for expert interpretation. As SIDER side effects terms belong to the Medical Dictionary for Regulatory Activities  [24] , a semantic similarity between these terms can be calculated based on the structure of MedDRA  [10]. Next, a hierarchical clustering method is applied to obtain 112 Term Clusters (TCs) which are then validated by experts  [23]. For instance, TC named 65_Dermatitis is the 65th TC and has Dermatitis as representative term.
Single-table datasets Single table datasets designed for DT learning represent each drug by an attribute-value vector. Four types of descriptors retrieved from NetworkDB are used to generate these attributes: the first is the class information, i. e. the studied SEP, the second one includes drug categories, the third one lists all drug targets with for each target, three attributes referring to the type of action of the drug (activation, inhibition and other) and the fourth concerns clusters of similar drugs according to the four similarity measures described above. Because of target and category multiplicity, the total dimension of this dataset varies between 741 and 924 depending on the SEP.
Decision trees Decision tree (DT) construction is a machine-learning method which uses (object×attribute) table to classify objects. Results given by this method are easily readable. Decision trees are built here with the J48 implementation of C4. 5 tree learner in the Weka toolbox using single table datasets converted into the ARFF format  [27]. We use the default parameters except for two of them: we use minNumObj = 5 and binarySplits = true.