dataesr/scientific-paragraphs-categorization huggingface dataset:
5 g/day is expected to result in no more than a 10 -5 lifetime cancer risk. Compounds which cannot be controlled to the TTC either require extensive follow-up testing with additional in vivo (geno)toxicology studies to determine their potential carcinogenic risk, or alternative synthetic routes can be considered which eliminates the compound of concern. There are 19 examples of intermediates/impurities in the GSK database with Ames-positive results and additional in vivo genotoxicity and/or carcinogenicity data (Table  5 ). Ames-positive degradation products and/or human specific metabolites represent another category of compounds which present a significant challenge. These compounds are often only identified later on during drug development. One such degradant is contained in the GSK database of Ames-positive compounds (Table  5 ).
There could also be sample bias associated with the number of tests of a particular assay type. It was noted that there was over-representation of carcinogens and of certain chemical classes in some of the databases, and that these databases consisted of a large number of old mammalian cell studies in which the criteria for positive or negative outcomes would most likely have been based on criteria no longer used. Breakout group 2 noted that for an Ames-positive result it is important to investigate the response pattern, e. g. , are the results positive in the absence or presence of S9, in which strains are the responses seen, and at which magnitude. Based on these results is it possible to generate a hypothesis why the Ames test is positive? Addressing such a question should include analysis of the chemical structure (are there structural alerts?), and try to understand if there are metabolic pathways that are unlikely to be relevant for humans (e.
Conclusions and recommendations In the final plenary discussion the workshop participants agreed that, based on the individual presentations and the initial analysis of results across the different databases, there appear to be patterns of results in mammalian cell tests that are consistent across databases and indicate whether an Ames-positive chemical is likely or not likely to be carcinogenic or genotoxic in vivo. In particular it appears that negative results in mammalian cell tests covering clastogenic/aneugenic and gene mutation endpoints could indicate that an Ames-positive chemical is not likely to be carcinogenic or genotoxic in vivo. However, there were relatively few Ames-positive chemicals in the individual databases that were negative in mammalian cell tests in vitro and for carcinogenicity.
There was consensus that because of their regulatory implications, these preliminary results were worth following up. Given that the individual databases contained overlapping chemicals with occasionally contradictory results, it was recommended that a consolidated database be constructed that would eliminate duplication, and that this be analysed. The construction and analysis of the consolidated database is reported separately  [52]. It was also acknowledged that, even if the analysis of the consolidated database shows that negative mammalian cell test results could be indicative that an Ames-positive chemical is not likely to be carcinogenic or genotoxic in vivo, it is unlikely that a firm conclusion could be reached, or in vivo testing be avoided, reduced or delayed, without further data.
Results from Japanese CSCL and ISHL databases of in vitro genotoxicity tests for chemicals tested either for carcinogenicity or in vivo genotoxicity (including chemicals overlapping with other databases). No. of chemicals showing in vitro profile of results/totalIn vitro results for Ames-positive chemicalsCarcinogenicity resultsIn vivo genotoxicity results (not tested for carcinogenicity) mammalian cell tests were available for 10 out of the 11 Amespositive substances: a test for gene mutation and either a test for clastogenicity (CAvit) or the in vitro MN test (MNvit). Long-term toxicity studies in rats and mice were available for all substances.
Acknowledgments The authors would like to thank  Dr. Panagiotis Karamertzanis  and  Pr. Frank Le Curieux  who kindly provided data from the ECHA public database on registered substances (http://echa. europa. eu/information-on-chemicals/registered-substances), which included dossiers for high tonnage chemicals, as well as for CMR (Carcinogen/Mutagen/Reproductive toxicant) chemicals that were submitted for the 2010 deadline.
1. 1 Are there any general trends that can be concluded from the data regarding the "risk" predicted by different patterns of in vitro results (whether mammalian cell tests are positive or negative in the face of Ames-positive results)? Do different patterns of in vitro results signify different follow-up approaches (particularly if and when in vivo tests are needed)? 2. What in vitro tests, if any, can be eliminated without diminishing the predictivity for in vivo genotoxicity or cancer of the standard battery? What in vitro tests can be added to help with the decision-making process? In what circumstances would you select additional in vitro studies, and which ones? Breakout group 1 looked at the data provided in the individ- ual databases and made some initial analyses. They noted the following: • The incidence of carcinogens among the Ames-positive results is ∼70%, but this includes genotoxic and non-genotoxic carcinogens • The incidence of Ames-positive non-carcinogens is ∼24% • The incidence of Ames-positive compounds with negative results in 2 mammalian cell assays is: -1.
Acknowledgments Most of the authors of this paper participate to the recently established  COST network, Action FA 1301-CephsIn-Action  (http://www. cost. eu/domains_actions/fa/Actions/FA1301). N. S. , M. K. , F. G. , F. M. collated the contributions from the April 2012 Meeting in Vico Equense that provide the ground for this work. P. A. , M. K. , N. S. and G. F. drafted and edited the final manuscript with comments from several authors. P. A. and G. F. conceived this work. G. F. and G. P. organised the meeting and provided assistance to the group on behalf of CephRes. D. M. has been supported by POR Campania FSE 2007-2013, Project  MODO -Model Organism. In addition, CephRes thanks the Municipality of  Vico Equense ,  Mrs Rosaria Savarese  for their hospitality and friendly assistance. Concetta Maffucci  and  Piero Amodio  assisted the CephRes team and the Authors of this paper and facilitated group meetings and discussions.
Acknowledgments We thank  Jean-Pierre RICHARD  for his contribution to the software design,  Jean-Pierre CAUDAL  for technical support,  Christine AUBRY  and  Christophe PETTON  for taking care of the birds, Amelie  LEPRINCE, Sabrina TARTU and Noemie TEMPLE  for their help during experiments, and  Ann CLOAREC  for English proofreading and editing.
This study was supported by a grant of the  French ANR (Agence Nationale pour la Recherche)  entitled '' Intermodaliteet imitation: aspects comportementaux et neuronaux (etude chez le bebeet les oiseaux)''  ( ANR-09-Blanc-0319-01 ). All authors are supported by the  University of Rennes  1 and by the  French CNRS. Audrey Perret  is a doctoral fellow of the  French Ministry of Higher Education and Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
How is variability considered in ecotoxicology literature? Our scientific literature is strongly dominated by the tyranny of the Golden Mean introduced by  Bennett (1987) , that restricts our vision of the dataset to a unique endpoint, and prevents us from fully exploiting the richness of this dataset. Variance is therefore considered at best as illustrative information, at worst as a noise, that prevents us from discerning/depicting the true biological effects, i. e. variations of mean along environmental gradients, still considered to be the most useful way to understand the studied phenomenon. In a recent synthesis,  Artigas et al. (2012)  proposed a framework for ecotoxicology that pointed out some new and some recurrent questions, including relevance of ecotoxicological tools. A key part of this relevance is to go beyond the question of biological endpoints by defining some quantitative parameters that we should focus on: the mean is of course a pertinent measure, but the variance is another, as well as differences of basal levels across populations and their range of variation.
Proteomics Cartilage and bone were carefully separated using a cutter and a hammer and immediately placed in liquid nitrogen. Frozen cartilage (200 mg) and bone (150 mg) samples were crushed in liquid nitrogen using a hammer and a stainless steel mortar and pestle and then ground to a fine powder in liquid nitrogen using a porcelain mortar and pestle. Protein extraction and quantification(2-D Quant Kit, Sigma Life Science), two dimensional electrophoresis (2-DE) and spot identification were then performed as previously described  [18]. Spot volume normalization was performed using the Progenesis "Total Volume Ratio" option. Descriptive statistics were done using R scripts and the ade4 package. Principal component analysis (dudi. pca) and between-class correspondence analysis (dudi. bca) were performed to investigate differences between OC-free and OC (D)-affected horses as well as heterogeneity between affected samples.
Comparative micro computed tomography (μCT) Cylindrical cartilage and subchondral bone explants were obtained from undamaged femoro patellar joints of both OC-free and OC(D)-affected foals using a 12mm punch. Samples were stored in 70% ethanol at 4°C before use. Scans were performed using approximately 15-μm voxel sizes (Phoenix nanotom™, GE measurement and control). The scanner used a tungsten source X-ray tube operating at 100 KV and 70 μA. Data acquisition from each sample required 90 minutes. Images were reconstructed using Phoenix datos*|x* CT soft-ware™. For measurements, the Microview™ software was used and the calibration for mineral density in the scans was performed with the use of a phantom (hydroxyapatite). For each sample, two regions of interest (ROIs) were designed manually corresponding to the subchondral bone area (less than 2 mm from the cartilage area) and to the trabecular area (between 3 and 4 mm under the cartilage area).
Morphometry parameters measured were: bone mineral density (BMD), bone volume fraction (BV/TV), trabecular thickness (Tb. Th), trabecular number (Tb. N), and trabecular spacing (Tb. Sp). A principal component analysis (dudi. pca) was done using R scripts and the ade4 package. Likewise, between-class correspondence analysis (dudi. bca) was performed to identify the most significant parameters differentiating OC(D)-affected and OC-free samples. Hierarchical clustering was performed using R (hclust), based on the 3 most significant parameters.
Metabolomics Synovial fluid was collected from tibiotarsal joints from OC-free and OC(D)-affected foals. For foals not presenting bilateral OC(D) lesions, synovial fluid was also collected from the contralateral OC-free joint. Samples were thawed at room temperature and 600 μL added to 5-mm NMR tubes prefilled with 100 μL deuterium oxide for field locking. The proton spectra were acquired at 500 MHz on a Bruker Advance III spectrometer, with the noesy1D sequence for water suppression. The temperature was 297 K. The time domain raw signals (free induction decays: FIDs) were collected on 32 K points for a spectral window of 6000 Hz and 64 transients. The FIDs were processed with the MestReNOVA software. The Fourier transform was performed with an exponential function producing a 0. 3 Hz line broadening.
Spectra were phased and the baseline correction was performed with three points at 0, 5, and 9 ppm. Each spectrum was calibrated using acetic acid signal at 1. 92 ppm in blood spectra. The spectral region between 0 ppm and 9 ppm was divided into 9000 spectral regions of 0. 001 ppm width called buckets. Each bucket is labeled with its median chemical shift value. The water region, between 4. 6 and 5 ppm, was excluded. Considering the possible variation of the sample concentration, the spectra were normalized according to the "Probabilistic Quotient Normalization" method  [19]. Statistical analyses consisted of a Principal Component Analysis (PCA) performed using the statistics SIMCA P + software (ver. 12, Umetrics AB, Umea, Sweden). The results are presented as a score plot constructed with the two principal components describing the spectra and the loading plots describing the importance of the variables (here, the buckets) to compute the principal components.
Validation by comparative μCT morphometry analysis of subchondral bone Bone mineral density (BMD), bone volume fraction (BV/TV), trabecular thickness (Tb. Th), trabecular number (Tb. N), and trabecular spacing (Tb. Sp) were measured on subchondral bone from the middle of femoral trochlea explants. These parameters were used for BCA analysis, highlighting BMD, Tb. N and Tb. Th as discriminating parameters. Hierarchical clustering based on these parameters distinguished OC-from OCD-predisposed samples and OC-free samples, in agreement with observed histological features and data obtained from proteomics. Again, OCD-predisposed samples (horses OC1, OC2 and OC4) were clustered with OC-free samples (Figure  5  and Additional file 1, microCt). Bone mineral density was significantly reduced in subchondral bone from OC-predisposed samples (horses OC3, OC4 and OC5; Figure  5 ).
Normalization of SAM files was done by randomly removing a given portion of the counts to achieve the same effective library size (H3K4me3 = 0. 7 million, H3K9Ac = 9. 8 million, H3K9me3 = 10. 8 million, H3K27me3 = 13. 9 million). Elements that showed differences in histone modification levels where annotated using the ABBlast search engine within the RepeatMasker program  (Smit et al. , 2010). The search was performed for both nucleotide and protein databases. Sequences without match where compared against the nucleotide database of NCBI using the blastn search engine and against the non-redundant protein database using blastx  (Altschul et al. , 1990).
COMPARATIVE ANALYSIS To find differences in the histone mark pattern between control and treated conditions, we used the Pyicoenrich function (with -binsize 0. 3) from Pyicos v2. 0. 6  (Althammer et al. , 2011). Visual inspection was performed on all the regions detected by the software with a z-score above 7. The GO enrichment analysis was performed using BLAST2GO  (Conesa et al. , 2005). For the analysis of significant differences in histone isoform enrichment in repeats, normalized read counts were compared using the DESeq package of the R software  (Anders and Huber, 2010). This package uses the negative binomial distribution to calculate the Pvalue for each element between two compared samples and find elements that present significant differences in their enrichment levels.
GO ENRICHMENT ANALYSIS The 60 genes for which chromatin structure differences had been detected were annotated using Blast2GO  (Conesa et al. , 2005). In order to check if GO terms would be overrepresented in this group of genes, 60 transcripts were randomly sampled out of 41,669 transcripts generated by a combination of RNA-Seq data and Sanger annotation. RNA-Seq data were generated from several biological replicates and these data will become available when the analysis is fully finished. The random samples were annotated as above and GO enrichment analysis was performed using Fisher's exact test implemented in Blast2GO and a p-value of 0. 2 as cutoff. Random sampling was repeated 3 times.
ACKNOWLEDGMENTS We are grateful to  Prof. Sam Loker  ( Albuquerque, NM, USA ) who attracted our interest to induced hycanthone resistance and  Prof. Michael J. Waring  ( Cambridge, UK ) for sharing his precious stock of hycanthone. Without his help this work would not have been possible since the drug is not anymore commercially available. This work received support from the  ANR   2010 BLAN 1720 01  (EPIGEVOL). The manuscript profited tremendously from the critical reading by two anonymous reviewers.
Acknowledgements. This work was financially supported by the  CNRS , the  University of Perpignan , the  University of Health Sciences  and the  National Agency Awards of Gabon. Thanks are due to  Lambert Ndong Obiang  and  Callixte Nkourou Mba  from the  Service d'Hygiene Publique of Gabon. The  French Laboratory  is a  World Health Organization Collaborating Center for Biological Control and Snail/Parasite relationships. The authors wish to report that there are no competing interests.
Acknowledgments The authors are grateful to the Tunisian authorities and thank all of the technical staff at the  Laboratory of Livestock and Wildlife  for their valuable help during this study. The authors would also like to thank  Dr. Julian Skidmore  and  Dr. Anthony Green  for kindly suggesting stylistic improvements to the text and  CIRAD (Centre de Cooperation Internationale en Recherche Agronomique pour le Developpement)  for coordination and administrative support.
Cloning of the glutamate gated chloride channel Two degenerate oligonucleotides were designed based on an amino acid region highly conserved in arthropod glutamate gated chloride channels (GenBank accession number: NP_001171232, NP_001071277, NP_001103244, NP_732447, ABI95855). The forward (PCRdegFor(d64)) and reverse (PCRdegRev(d16)) corresponds to MEYSVQLTFRE and KTNTGEYSC amino acid sequences respectively. The reverse transcription reaction was performed in 5 mg of total RNA primed with oligodT and random primers using Superscrit II kit (Invitrogen). KOD Hot Start DNA polymerase (Novagen) was used for PCR amplification and the amplicon was subcloned in pGEM-T (Promega) vector and analyzed by sequencing. The 59 cDNA was obtained by 59RACE and inverse PCR based on the protocol published by Huang, which combines the template-switching effect with inverse PCR  [18].
Voltage-clamp in Xenopus laevis oocytes Defolliculated oocytes were injected with 5 ng, 10 ng and 20 ng of each cRNA and kept at 16uC in modified Barth's solution for 5 days. Two-electrode voltage clamp recording were performed at room temperature using TURBO TEC-10CX (npi electronic GmbH, Tamm, Germany) and PClamp 9 software (Axon Instruments, USA), from 2 to 4 days after oocyte injection. Oocytes were recorded in a chamber (Model RC-1Z, Warner Instrumenst, USA) under continuous superfusion. Electrodes were pulled to 0. 5-2 MV and filled with 3 M KCl. The bath reference was 3 M KCl in a 3% agar bridge connected to an Ag-AgCl pellet. Currents were recorded in response to a ramp protocol consisting in a holding potential period of 230 mV for 40 ms followed by a voltage jump to 2100 mV for 20 ms.
The ramp was from 2100 mV to +60 mV with a 360 ms duration. The data were filtered at 1 kHz, digitized using a digidata 1440A analogueto-digital converter and analyzed using Axon pClamp 9 software. The solution bathing the oocytes during electrophysiological recordings had the following composition (mM): 115 NaCl, 2 KCl, 1. 8 CaCl 2 , 1 MgCl 2 , 10 HEPES pH 7. 5 obtained with NaOH. For the low [Cl 2 ] o solution all NaCl was replaced with the gluconate Na salt. Stock solutions of the drugs in DMSO were: emamectin 10 mM, ivermectin 10 mM, picrotoxin 100 mM. Different concentrations of the drugs were obtained by dilution in bathing solution. The highest DMSO concentration used was seen to have no effect on CrGluCl currents. Analysis of concentration-dependence of glutamate or drug effects was done by fitting a Hill model to the data using the non-linear regression suite of Sigmaplot 12.
Molecular modelling of CrGluCla structure In the absence of structural data for the GluCl receptor of Caligus rogercresseyi (CrGluCla), we built a model to help our understanding of experimental results. CrGluCla shares a 53% sequence identity with CeGluCla whose high resolution X-ray diffraction structure has been resolved  [16]  (comparison of similarly truncated CrGluCla as crystallized CeGluCla). We used these data (PDB ID 3RHW) as a reference structure to build a homology model of CrGluCla using Modeller v 9. 10 software  [19]. The molecular model was embedded into a 1306130 A POPC lipid bilayer in a water box. The hydrated system was neutralized with NaCl at a concentration of 150 mM. The system was submitted to a molecular dynamics simulation under periodic bordering conditions (13561356165 A ˚3) and isobaric-isothermal ensemble (NPT).
Docking simulations The homology model structure was used in docking simulations to identify ivermectin and emamectin binding sites in CrGluCla. The molecular docking procedure used Glide  [21]  software of the Schro ¨dinger suite. The drugs, that belong to the macrocyclic lactone group, were designed using Maestro 2D sketcher  [22] , and prepared with LigPrep  [23]. The CrGluCla model was prepared using the Protein Preparation Wizard  [24]  panel, assigning bond order, adding missing hydrogen atoms, correcting metal ionization states and creating disulphide bonds. Grid size and position was defined from the position of ivermectin observed in the CeGluCla-ivermectin complex crystal structure. The box centre was defined as the midpoint of a line joining the two most distant atoms of the ligand core.
Molecular dynamics Three systems were considered: CrGluCla, and the CrGluClaivermectin and CrGluCla-emamectin complexes. Each system was embedded into a POPC lipid bilayer in a water box (dimensions: 13561356165 A ˚3) and a NaCl concentration of 150 M. NAMD software  [20]  was used to relax each system through molecular dynamics (MD) simulations. MD was performed with the CHARMM force field and the TIP3P water model  [25]. The temperature was kept at 300uK with the isobaric-isothermal ensemble using Langevin dynamics with a damping coefficient of 1 ps 21. By means of the Langevin Piston method the pressure was fixed at 1 atm. The equations of motion were integrated employing the Verlet r-RESPA algorithm  [26]  with a time step of 2 fs. All systems were subjected to energy minimization using periodic boundary conditions.
Cloning of the glutamate gated chloride channel The GeneBank database was explored for the presence of sequences for glutamate-gated chloride channels belonging species phylogenetically close to Caligus rogercresseyi. The search produced sequences for Lepeoptheirus salmonis, Drosophila melanogaster, Nasonia vitripennis, Apis mellifera and Tribolium castaneum, which were used to design degenerated primers to attempt cloning a fragment of the receptor from C. rogercresseyi total RNA. The PCR amplification with the degenerated primers (Table  S1 ) gave rise to a product of approximately 450 bp which included the two extracellular domain Cys-loops characteristic of these receptors. The predicted amino acid sequence of this fragment showed a 90% identity with the sequence from L. salmonis and 74% with that of D.
Molecular modelling of C. rogercresseyi GluCla CrGluCla shares a high percentage of sequence identity with CeGluCla whose X-ray structure has been recently solved  [16]. We have used this structure to build a homology model for the Caligus receptor to be subsequently used in docking of emamectin and ivermectin followed by molecular dynamics simulations. A view of the resulting modeled structure can be seen in Figure  S4. All main features of the reference structure such as secondary structure and side chain conformation were retained in the molecular model of CrGluCla, as expected from the high conservation of sequence particularly in the transmembrane domains. In order to validate the molecular model and protocol to be used we performed a docking assay with the CeGluCla crystal structure (PDB code 3RHW) and ivermectin.