dataesr/scientific-paragraphs-categorization huggingface dataset:
Setup We use the two following SPLs from FeatureIDE  [21] : 1. E-Mail. This SPL gathers a family of systems managing mails. Examples of optional features in this SPL include encryption or the address book. 2. GPL. The Graph Product Line is a family of graph manipulation algorithms  [17]. We choose these particular SPLs as they are considered to be standard benchmarks. Table  2  gathers detail regarding these two SPLs. In particular, for each SPL, it presents the programming language in which it is implemented, the number of features of the corresponding FM, the number of possible product variants that can be built according to the FM and the number of input products that have been used by ExtractorPL to extract the SPL. Indeed, we used a sample of products to extract the SPL since hundreds of products can be build from these SPLs.
The configuration of the products used as input by our approach are configurations randomly selected from the space of all the possible configurations that can be generated from the FM  [11]. For each configuration randomly generated, we use FeatureHouse to construct the corresponding software product variant. The resulting products variants are then used by ExtractorPL to reverse-engineer the SPL. For each SPL and for each number of products used by our approach, the extraction of the SPL has been independently performed 10 times. In the following, we present two approaches to automatically evaluate the resulting SPL. The first one compares the products generated by ExtractorPL with the ones used to extract the SPL. We expect the resulting SPL to be able to build the products that were used as input.
Decision Tree Model The decision tree describing possible outcomes and medical management decisions within a period of 1 year is shown in Fig. 1. Pharmacoeconomic analyses were conducted using the software Microsoft Excel Ò , version 2007 (Microsoft Corporation, Redmont, WA, USA). Following initial treatment, patients were assumed to achieve remission or to change therapy. After achieving remission, patients could either relapse or remain on maintenance therapy until the end of the 12 months. Patients who relapsed or did not achieve remission, either due to lack of efficacy or lack of tolerability, moved to a second therapeutic step, which include one of the four following strategies if treatment was not effective: (1) increase the dose of the current therapy (titration); (2) receive another antidepressant from the same class or from another class (switch within the class or to another class); (3) add another therapy to increase the efficacy of the initiation drug, including combination with bupropion, combination with psychotherapy, or augmentation with quetiapine (add-on); or (4) switch to psychotherapy.
Clinical Inputs Rates of remission on the initiated treatments were derived from the meta-analysis conducted by the TLV (see Wessling and Ramsberg, Appendix 11  [1]  for details), which considered studies defining remission as a score of 7 or lower on the 17-item Hamilton Depression Rating Scale (HAMD) or a score of 12 or lower on the 10-item Montgomery-A ˚sberg Depression Rating Scale (MADRS). Data related to the risk of relapse, efficacy following therapeutic change, and risk of suicide attempts and related death were derived from the published literature and expert opinion. The rate of remission in patients on escitalopram was estimated at 47. 56 %. The range for the seven comparators was 40. 21 % (fluoxetine) to 45. 68 % (venlafaxine) (Table  1 ). The proportion of patients experiencing relapse was assumed to be similar across treatments and was set to Fig.
Medical Management Given the absence of therapeutic guidelines and published data related to the treatment of MDD patients in Belgium, the results of a Belgian survey were used to simulate the evolution of patients through the decision tree (Table  2 )  [14]. In this questionnaire, physicians were first asked which treatment class (SNRIs or SSRIs) they more often used as an initial therapeutic step; they were then asked to consider subsequent treatment steps according to the reason of treatment change (lack of efficacy or tolerability issues). The choice of therapeutic changes (switch, titration, add-on, or transfer to physiotherapist) and time to events (from initiation to second step, and from second step to third step) were also collected. Data from 97 questionnaires completed by general practitioners (GPs) were used to inform the model.
Therefore, the results cannot simply be reported on a tornado diagram  [45]. For this study, we decided to conduct a limited scenario analysis to assess the sensitivity of the results to parameters for which no strong data were readily available (probability of suicide following failure to achieve remission or relapse, costs of hospitalization and costs of absenteeism). Parameter uncertainty was then analyzed through PSAs (e. g. , overlapping of the confidence intervals between remission rates from the TLV metaanalyses, variation between clinician answers in the Belgian survey). Finally, in order to not add uncertainty in the model, no subgroup analysis, which would have required additional assumptions and data, was conducted.
Acknowledgments The study was funded by  Lundbeck SAS. Pr. Lieven Annemans  received an unrestricted grant from  Lundbeck. Melanie Brignone  and  Ann De Pauw  were employed by  Lundbeck. Sylvain Druais  and  Aline Gauthier  received funding for this project by  Lundbeck. Pr. Koen Demyttenaere  received fees from Lundbeck.
(2006)  onto which we apply an extinction from  Fitzpatrick (1999). Even if this AGN template significantly improves our fitting, we decided to discard this template from our library for the following reasons. First, the inclusion of an extra parameter (the extinction) decreases the number of sources on which our fitting can be applied. Second, the Richards template presents an odd, and unrealistic tail in the far-IR (probably due to the poor far-IR coverage of the dataset used to build this template). Third, while increasing the scatter in L IR AGN , we find no drastically different results from using the built-in AGN template. Finally, being an average template, star formation can still contribute at long wavelength and we would therefore overestimate the AGN luminosity.
Conclusion We present new Herschel and sub-mm observations for the HeRG É sample consisting on 70 powerful radio galaxies spanning 1<z<5. 2. Complemented by other data sets, we now have continuous coverage of the IR spectral energy distribution over the range from 16-870 µm. All galaxies in our sample have integrated IR luminosities L IR > 10 12 L , classifying them as ULIRGs, while half of all the sources at z>2 have L IR > 10 13 L and are HyLIRGs. We use the DecompIR code to decompose the IR SEDs of galaxies in our sample in a robust and uniform way into an AGN and SB components. To make these fits, we assume a single AGN template and a variety of starburst templates. Our results for the AGN contribution are conservative in that we assumed a single template and it is possible that this template could lead to an underestimate of its contribution to the IR SED.
Acknowledgements. GD warmly thanks Clive Tadhunter and Rob Ivison for their comments allowing a significative improvement of this paper. GD thanks also the referee for his comments helping to clarify this paper. NS is the recipient of an     observed wavelength. This correction factor can be as small as 2 and as high as 20 for X-rays, depending mainly on the morphology. We here want an approximation of this factor for our sources. First, we can use a purely geometrical approach. Assuming the torus around AGN to be optically thick at all wavelengths, it absorbs light from across the electromagnetic spectrum and re-radiates in IR. We can use the statistical distribution of type 1 and type 2 AGN in the sky  (Barthel, 1989). The solid angle for an opening angle of 45 • corresponds to a factor ∼2.
Activity Measurements and Determination of Apparent Kinetic Parameters-The catalytic activity of wild-type CrPGK1 and mutants was measured spectrophotometrically by a coupled enzyme assay with CrGAPA. The reaction mixture contained 30 mM Tris-HCl, pH 7. 9, 10 mM KCl, 4 mM 3-PGA, 5 mM ATP, 20 g/ml CrGAPA, and 0. 15 mM NADH. The reaction was measured at 25 °C and initiated by the addition of CrPGK1. Activity was calculated from the decrease in absorbance at 340 nm (i. e. NADH oxidation). The apparent kinetic parameters (K m and k cat ) for ATP and 3-PGA of reduced or oxidized wildtype CrPGK1 were determined at a fixed saturating concentration for one substrate and varying the concentration of the other substrate. Three independent experiments were performed at each substrate concentration, and the apparent K m and k cat values were calculated by nonlinear regression using the Michaelis-Menten equation with the program CoStat (CoHort Software).
Redox Treatments of Wild-type CrPGK1 and Cysteine Variants-Purified wild-type CrPGK1 and cysteine (CrPGK1-C108S, CrPGK1-C227S, and CrPGK1-C361S) were incubated at 25 °C for 30 min in the presence of 10 mM reduced DTT (DTTred) or 10 mM oxidized DTT (DTTox). After incubation, protein samples were assayed for PGK activity and separated by non-reducing SDS-PAGE (12% gel) and stained with Coomassie Brilliant Blue. The data were represented as the percentage of maximal PGK activity obtained by incubating the protein with DTTred. Incubation of CrPGK1 with Chloroplastic TRXs from C. reinhardtii-Activation treatments of purified CrPGK1 WT was carried out incubating the enzyme in the presence of chloroplastic TRXs from C. reinhardtii supplemented with 0. 2 mM DTTred. Control experiments were performed in the presence of 0.
The width of DLS hydrodynamic diameter distribution is indicated by the polydispersion index. In the case of a monomodal distribution (Gaussian) calculated by means of cumulant analysis, polydispersion index ϭ (/Z avg ) 2 , where is the width of the distribution, and Z avg is the average diameter of the protein population, respectively. Three-dimensional Modeling and Molecular Dynamics-The tridimensional model of the mature CrPGK1 protein (accession number A8JC04, 401 amino acids) was obtained using Swiss-Model software  (31). The template was automatically searched with BLAST and HHblits, and the crystal structure of PGK from Bacillus stearothermophilus (PDB code 1PHP) was selected on the basis of sequence identity. Using the coordinates of the tridimensional model of CrPGK1, its oxidized form was obtained via energy minimization by molecular mechanics using the AMBER software  (32).
Therefore, in the oxidized CrPGK1, the underivatized Cys 108 probably attacks the Cys 227 -Cys 361 disulfide bond during denaturation and/or digestion of the protein and leads to the formation of the arti-factual disulfide bond between Cys 108 and Cys 361. Taken together, these results indicate that CrPGK1 contains a single disulfide bond between Cys 227 and Cys 361 , which is responsible for the TRX-dependent redox regulation. CrPGK1 Is a Monomeric Enzyme with Two Distinct Domains-The biological conformation of CrPGK1 was evaluated by DLS measurements. The majority of structurally known PGKs are monomeric except PGK from Plasmodium falciparum (PDB code 3OZA (  42 )) and from Pyrococcus horikoshii OT3 (PDB code 2CUN)  5  that are dimeric. The mean hydrodynamic radius measured for CrPGK1 was 3.
39 Ϯ 0. 2 nm (mean Ϯ S. D. of three independent experiments), corresponding to an apparent molecular mass of 58. 8 kDa. This value is close to the molecular mass derived from the sequence (49. 0 kDa), strongly suggesting that CrPGK1 is also a monomeric enzyme. Because the atomic structure of CrPGK1 has not been determined, it was modeled on the basis of the crystal structure of B. stearothermophilus PGK (PDB code 1PHP (  43 )) sharing a sequence identity of 60% with CrPGK1. The obtained model corresponds to the reduced form of the enzyme because the reference structure has only one cysteine that does not display a conserved position in CrPGK1 (supplemental Fig. S1 ). The model building predicts a monomeric state for CrPGK1, in agreement with DLS results. Similarly to already known PGKs, the enzyme from Chlamydomonas is composed of two distinct domains, an N-and a C-terminal domain, both displaying a.
Number of free thiols in CrPGK1 WT and cysteine variants under both native and denatured conditions Purified recombinant proteins (5-10 M) were incubated with DTNB as described under "Experimental". The thiol content per protein was determined under both native and denatured conditions. CrPGK1 WT was assayed under both native (i. e. untreated) (WT) and reducing (WT red ) conditions. Data are represented as mean Ϯ S. D. (n ϭ 3). ND, not determined. It has been previously demonstrated that the regulation of the four CBC enzymes undergoing dithiol/disulfide interchanges is preferentially controlled by f-type TRX, which is considered the specific redox mediator of the photosynthetic carbon fixation pathway  (2, 6, 7). Redox activation of CrPGK1 was also found to be specifically controlled by f-type TRX because other chloroplastic TRX types proved inefficient.
This constraint may hinder the domain rotation necessary to reach the closed conformation. The superposition of human PGK in the closed conformation (PDB code 2WZB (  48 )) with the CrPGK1 models in both reduced and oxidized forms showed that the oxidation originates a displacement of strands ␤9 and ␤10 belonging to the central sheet in the opposite direction with respect to the movement required for enzyme closure (Fig. 10 ). On this basis, it can be speculated that the lower catalytic efficiency of the oxidized enzyme is caused by its difficulty to achieve the closed conformation. A model summarizing these properties and the structural features of CrPGK1 redox regulation is presented in Fig. 11. The redox sensitivity of chloroplastic PGK from Chlamydomonas, representing the fifth enzyme known to be redoxregulated by TRX-dependent mechanisms, constitutes another step toward the understanding of the regulation of the CBC.
Search for trials Literature searches in PubMed, American Society of Clinical Oncology (ASCO), and European Society of Medical Oncology databases were performed. Searches were limited to human studies and English-language publications. For PubMed database research, the following strategies were used: ("cholangiocarcinoma" OR "biliary tract carcinoma" OR "biliary tract cancer" OR "gall bladder cancer" OR "gall bladder carcinoma") AND (((gemcitabine) AND oxaliplatin) OR ((gemcitabine) AND "cisplatin")). The main keywords used for the search on ASCO and ESMO database are cholangiocarcinoma and chemotherapy.
Data extraction Two authors (F. F. , M. J. P. ) independently extracted information using predefined data abstraction forms. The following details were extracted: type of study, year of publication, study period, number of centers, nationality of the centers, follow-up, eligibility criteria, doses of chemotherapy, treatment schedule, duration of the treatment, patients' characteristics (age, sex, extent of disease, primary tumor site, WHO-PS, metastatic sites), primary endpoint and its definition, secondary endpoints and their definitions, overall survival (definition, median, and 95% confidence interval), progression-free survival (definition, median, and 95% confidence interval), and grade 3 and 4 toxicity data. Missing data from studies deemed potentially eligible were sought from the authors via e-mail request.
Toxic effects according to the National Cancer Institute's Common Toxicity Criteria for Adverse Events grade 3 and 4 were pooled and compared within each arm. In order to assess the internal validity of our results, these analyses were repeated 1000 times with the use of bootstrap sample to derive 95% confidence interval for the mOS and mPFS in Gemox and Gemcitabine/CDDP groups. P-value of 0. 05 or lower was considered as statistically significant. Analyses were conducted with the use of SAS software, version 9. 2 (SAS Institute Inc. , Cary, NC), and R software (version 2. 10. 1).
Finally, fluvastatin increased worm mean lifespan by 14% at 20 mM and by 13% at 50 mM (Figure  6L ), indicating that the decrease in respiration induced by fluvastatin has an overall beneficial effect in worms. A potential mechanism to explain the decreased respiratory capacity in cells and worms after long-term statin treatment is the inhibition of mevalonate synthesis, which in turn limits CoQ10 and heme-A levels  28  , two essential components of the mitochondrial respiratory chain  35. Another possibility is that these effects are mediated at the transcriptional level. To test this hypothesis, we analyzed a dataset from the Connectivity Map database (http://lincs. hms. harvard. edu/db/). This data set contains gene expression profiles of the human breast cancer cell line MCF-7 exposed for 6 hours to three different statins, i.
Screening of a 1,200 drugs library -specific procedures. Hepa1. 6 cells were seeded at 8,000 cells per well, in a volume of 20 ml, in 384-well plates, treated on day 1 using a Sciclone G3 Liquid Handling Workstation (PerkinElmer, Waltham, MA, USA) and distributing a volume of 2. 22 ml of compounds at 100 mM to obtain a final concentration of 10 mM in each well. Assays were performed after 24 hours of incubation with compounds. For ATP level, redox potential and TMRM assays, we used DMSO at 0. 1% as a negative control (final DMSO concentration in all wells) and oligomycin A at 1 mM as a positive control, both of them being present on each side of all the plates, i. e. columns 23-24 and 1-2 respectively. These assays were performed in duplicate. High-throughput screening data were analyzed using the cellHTS2 package from R software (http://www.
This threshold was arbitrarily set at ŝ2 §0:005 for ATP, TMRM and redox potential signals. Clustering of the 53 hits was performed on the Hierarchical Clustering module available on the GenePattern website (http://www. broadinstitute. org/cancer/software/genepattern), using Euclidean distance and single linkage as analysis parameters. Measurement of cellular NAD 1 and NADH levels. NAD 1 and NADH levels were determined using the EnzyChrom TM NAD/NADH Assay kit and following manufacturer's instructions (BioAssays Systems, Hayward, CA, USA) Measurement of DNA levels per cell. DNA was measured directly in Seahorse 96well plates using the CyQUANTH Cell Proliferation Assay Kit and following manufacturer's intructions (Lifetechnologies, ThermoFisher Scientific, Waltham, MA, USA). Measurement of cellular and C.
Worm statistics. Survival analyses were performed using the Kaplan Meier method and the significance of differences between survival curves calculated using the log rank test. Calculation of mean lifespan and SEM were calculated using the R software. Bioinformatics analysis. The datasets generated in human lymphoblastoid cell lines  36  was downloaded from Gene Expression Omnibus under the accession number GSE36868. The datasets generated in the human breast cancer cell line MCF-7 was downloaded from the Connectivity Map database (http://lincs. hms. harvard. edu/db/). Data were first normalized with RMAExpress software (http://rmaexpress. bmbolstad. com/) and analyzed using the Gene Set Enrichment Analysis software  50. The cholesterol synthesis geneset was defined according to GO annotation (Accession number: 0006695).
01) following sterol depletion. (C) Negative enrichment score for mtDNA transcription genes (geneset GO:0006390, q50. 24) shows that mtDNA processing is downregulated after statins treatment in MCF-7 cells. (D-F) Effect of simvastatin on gene expression in lymphoblastoid cell lines derived from patients after 24 hours treatment at 3 mM. (D) Vulcano plot of microarray dataset. Cholesterol synthesis geneset (GO:0006695) and mitochondria geneset (GO:0005739) are represented in red and blue, respectively. The red horizontal bar corresponds to the p-value for multiple testing after Bonferroni correction. (E) Positive enrichment score indicates that in lymphoblastoid cells simvastatin induces cholesterol synthesis genes (geneset GO:0006695, p,0. 001). (F) Negative enrichment scores for mtDNA transcription genes (genesets GO:0006264, q50.
Acknowledgments We thank  Gerard Turcatti ,  Marc Chambon , and  Pierre Go  ¨nczy for sharing reagents and equipment, the  Caenorhabditis Genetics Center  for providing worm strains and the Auwerx team members for discussions. Statistical advice by  Prof. Stephan Morgenthaler  ( Mathematics Institute for Analysis and Applications, EPFL ) is highly appreciated. LM is supported by the '' Fondation pour la Recherche Medicale '' and RHH by a  VENI grant  from  ZonMw  (number  91613050 ) and an  AMC Postdoc fellowship. JA is the NestleChair in Energy Metabolism. This work is supported by grants of the  Ecole Polytechnique Federale de Lausanne , the  EU Ideas program  ( ERC-2008-ADG-231138 ), the  NIH  ( 1R01HL106511  and  R01AG043930 ), the  Velux Stiftung , the  Swiss National Science Foundation  ( 31003A-124713  and  31003A-125487  and  CSRII3-136201 ).
q and e are normalized such that max| q|=1 and 1 2 ε|e| 2 dV = 1. ∆ε 12 = ε 1 -ε 2 is the difference in permittivity between the materials on either side of the boundary and ∆ ε -1 12 =ε -1 1 -ε -1 2. Here we are only interested in the geometric contribution to the g om , so ε 1 is simply n 2 over the entire disk, while ε 2 =1. g geo om is computed from Eq. 15 using a FEM simulation software (COMSOL Multiphysics). The results for the four mechanical modes shown in Fig. 5  are summarized in Table  3. For a given mechanical mode, the displacement of every point of the disk is spatially non uniform and g geo om = - dω geo 0 dx is therefore dependent on the somewhat arbitrary choice of the reduction point which experiences the displacement dx. The normalization choice max| q|=1 in Eq. 15 means that the point of maximal displacement is used as reduction point.
DNA was then re-suspended by incubation in 200 ml water at 60 °C. Samples are quantitated and quality assessed by absorbance measurements at 260 and 280 nm. Genotyping was performed by personnel unaware of clinical status or histology of patients. TM6SF2 rs58542926, NCAN rs2228603 and PNPLA3 rs738409 genotyping. TM6SF2 rs58542926, NCAN rs2228603 and PNPLA3 rs738409 genotypes were determined by allelic discrimination using TaqMan reagents (Applied Biosystems Inc. , USA) according to the manufacturer's protocol. Control samples of known genotype were also included in every 96-well plate (blank, homozygous wild-type, homozygous mutant and heterozygous). Statistical analysis. Statistical analyses were performed using SPSS v19. 0 (IBM, USA) to collate and analyse cohort phenotype data. Continuous variables were tested using Student's t-test/one-way analysis of variance and categorical variables by w 2 -squared test unless otherwise stated.
Acknowledgements Q. M. A. is the recipient of a  Clinical Senior Lectureship Award  from the  Higher Education Funding Council for England (HEFCE). Aspects of this work have been supported by the facilities of the  Newcastle National Institute of Health Research (NIHR) Biomedical Research Centre  and by the  'Fatty Liver Inhibition of Progression' (FLIP)  project funded by the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement Health -F2-2009-241762.