dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). Since is it very difficult to get reliable data on entire fish populations, most AOP-related field studies use population models  (Kramer et al. , 2011; Miller et al. , 2015). The present study was not initially designed to study adverse outcome pathways, since most experiments were carried out when the AOP theory as such was not yet published  (Ankley et al. , 2010) , but this dataset still contains interesting information on indirect relationships among molecular markers, key events and ecological data. In addition, the study is an example of how AOP could be applied in an environmental context and field studies like the present may point towards the potential to provide qualitative and quantitative predictive modelling. Complex environmental mixtures are most probably able to cause an interplay between multiple pathways potentially contributing to an AO  (Groh et al, 2015).
that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: In summary, although no complete AOP could be observed in the dataset of the Amsterdam City Fish research, indirect relationships linking pollutant exposure via MKE to impaired locomotion were demonstrated at the most polluted site. When evaluating the entire dataset, both fish biomarkers and a bioanalytical monitoring strategy (SIMONI Risk Indication) seem to be valuable indicators of risks to macroinvertebrate and fish populations due to micropollutants. J o u r n a l P r e -p r o o f.
Acknowledgements This work is dedicated to the memory of our friend and colleague, the late  Frank Verweij , who performed and coordinated the field work and laboratory biomarker analyses for this project. The research was funded by the  City of Amsterdam , the  European Union  ( CITYFISH  project  EVK1-CT-1999-00009 ) and the  Waternet Institute  for the  Urban Water Cycle, Amsterdam. Laura Moria ,  Tim Pelsma ,  Gerard ter Heerdt (all Waternet ) are acknowledged for critically reading the ecological parts of the manuscript and providing additional information on WFD ecological quality scores. Gert van Ee (HHNK water board), thanks for providing information on macroinvertebrate studies at the VM site. Tim Williams  ( University of Birmingham, UK ), thanks a lot for critically reading and improving the initial manuscript.
Methods The molecular structure of IBF and NCTA were retrieved from National Institute of Standards and Technology (NIST) Reference Database Number 69 (https://webbook. nist. gov/chemistry). The molecules were optimized using the DMol 3 DFT package. We employed the Generalized Gradient Approximations (GGA) with Perdew-Becke-Ernzerhof (PBE) functional  [22]  including implicit solvent  [23]  as described by COnductor-like Screening MOdel (COSMO)  [24]. The choice of PBE in current DFT calculations is based on (1) our analysis of computational works reported in literature investigating properties of IBF  [25] [26] [27] [28] [29]  and NCTA  [28] [29] [30] [31] [32]  and (2) recommendation in Ref. [33]. To control the convergence behaviour for an enhanced self-consistent field calculations  [34, 35] , thermal smearing  [36]  was also applied including double numerical basis including d-polarization function  [37]  level of theory.
Analysis strategy The search strategy selects events with a final state containing one prompt photon and the decay products of a top quark, namely an electron or a muon, a b-tagged jet, and missing transverse momentum, and estimates contributions from FCNC processes in this background-dominated dataset. A signal region (SR) is defined by loose requirements on the kinematic properties of the final-state objects, giving rise to a large acceptance for signal events in the production mode. With this selection, the search is most sensitive to FCNCs in this mode, but the decay mode is included in the analysis. The main background contribution stems from electrons misidentified as photons, primarily in top-quark-anti-top-quark events (t t). These contributions and contributions from hadrons misidentified as photons (both labelled "fakes" in the following) are modelled by Monte Carlo (MC) simulations and scaled to data-driven estimates.
The difference between the efficiencies for the LH and the RH couplings is due to the kinematic distributions of the W boson's decay products, which differ depending on the handedness of the top quark. The efficiencies for the decay mode are 0. 45% and 0. 51% for the tuγ and the tcγ coupling, respectively, and are lower due to the requirement of not more than one jet in the final state, i. e. this analysis is optimised for the production mode. The absolute statistical uncertainties in the efficiencies are 0. 03% or smaller. In the SR, 9557 data events are selected. The ratio of production-mode to decay-mode event yields is 4. 2 (5. 3) for the LH (RH) tuγ coupling and it is 0. 86 (0. 68) for the LH (RH) tcγ coupling, i. e. in the case of tuγ coupling, the dominant signal process is indeed the production mode.
Insulin clearance (MCR I ) during the fasting state was calculated as fasting ISR divided by FPI concentration, because at steady state R dIns 5 ISR. During the OGTT we used a single pool model to describe insulin kinetics, as previously proposed  (23) , where the input is the ISR obtained by deconvolution of the plasma C-peptide concentration curve (calculated as described above). The formulas to calculate the rate of insulin extraction R dIns (t) and insulin clearance MCR I (t) and at each time point of OGTT are given by where V Ins is the volume of distribution of insulin which was previously estimated as 141 mL/kg  (24) (25) (26). To estimate temporal changes in insulin extraction and clearance, insulin concentrations were first smoothed by spline fitting using Data Curve Fit Creator (SRS1 Software, Newton Centre, MA), and dI(t)/dt was calculated from fitted data using a 5-min interval.
0001). A parametric model was developed for LCA based on the dietary net energy fluxes in a pig system. A nutritional pig growth tool, InraPorc ® , was included as a module in the model to embed flexibility for changes in feed composition, animal performance traits and housing conditions and to simulate individual pig performance. The comparative individual-based LCA showed that LRFI had an average of 7% lower environmental impacts per kilogram live pig at farm gate compared to HRFI (P < 0. 0001) on climate change, acidification potential, freshwater eutrophication potential, land occupation and water depletion. High correlations between FCR and all environmental impact categories (>0. 95) confirmed the importance of improvement in feed efficiency to reduce environmental impacts. Significant line differences in all impact categories and moderate correlations with impacts (>0.
Experimental data The experimental data were obtained from the fifth generation of Large White pigs divergently selected for RFI. The selection process and results concerning low RFI (LRFI, more efficient pigs) and high RFI (HRFI, less efficient pigs) lines are reviewed in  Gilbert et al. (2017). The present data set includes 60 male pigs in the LRFI line and 58 male pigs in the HRFI line. Growing pigs had ad libitum access to a one-phase conventional diet (Table  1 ). The experimental data were collected from birth to slaughter. Body weight was recorded at birth; at weaning (average 28 days of age); at the beginning of the fattening period (10 weeks of age); at 11, 15, 19, and 23 weeks of age; and at the end of the test (target BW 115 kg). During the fattening period, data on individual daily feed intake (DFI) recorded on ACEMA 64 automatic feeders (ACEMO, Pontivy, France) were available, and back fat thickness (BFT) was measured by ultrasound on live animals at 23 weeks of age, using an ALOKA SSD-500 echograph (Aloka, Cergy Pontoise, France).
Model structure The model consists of six modules with distinct functions. Feeding plan module. InraPorc ® , which is a model and software designed to simulate the performance response of pigs to different nutritional strategies  (Dourmad et al. , 2008; van Milgen et al. , 2008) , was incorporated in the LCA model to benefit from its features. It contains the licensed INRA-AFZ database of characterised feed ingredients  (Sauvant et al. , 2004)  as an embedded library. This library distinguishes different nutritional values depending on the animal physiological status (sows and growing pigs). In the feeding sub-module, the composition of the diet and the feeding plan (rationing and sequencing plan) during the different periods of the animal's lifetime were defined based on experimental data.
The outcome of this sub-module is the chemical compositions and nutritional values of the diets, based on the INRA-AFZ database. Animal profile module. Each animal profile is the compilation of the feeding plan, housing conditions, experimental data, NE system and a final calibration in InraPorc ®. The Gamma function was used to express ad libitum feed intake because of its flexibility which enables it to adjust to changes in feed intake and BW  (van Milgen et al. , 2008). The daily ad libitum feed intake and NE of the feed characterised the animal daily NE requirements. InraPorc ® was used to establish the individual profiles for each pig separately in the lines during the fattening period (day 68 to day 179), based on the animal's individual data, which were recorded daily, as previously proposed by  Saintilan et al.
, 2015. The average profiles for groups of sows and their piglets were defined separately in InraPorc ® based on the experimental data on the average HRFI and LRFI sows/litters performance summarised by  Gilbert et al. (2012). The outcome of this module is the predicted growth performance (average daily gain (ADG) and average daily feed intake (ADFI)), PD and LD during fattening, respectively, the ratio of body protein to and body lipid (BP/BL ratio) and mineral excretions of the pigs. As InraPorc ® was not designed to model the performance of animals post-weaning, a calculation module was developed in R to estimate the excretions and emissions during the postweaning period (28 days to 10 weeks of age), according to  Rigolot et al. (2010a and 2010b). Emission and excretion module. To calculate the emissions and excretions, and the slurry composition, three submodules were developed in R for the sow-litter, postweaning and fattening stages.
The average performance data were used for the sow-litter stage and the individual performance data were used for the post-weaning and fattening stages. The components of the excreta (DM, organic matter (OM), nitrogen (N), phosphorus (P), potassium (K), copper (Cu) and zinc (Zn) were calculated using the massbalance approach, as the difference between nutrients taken up from the feed and the nutrients retained in the body. Environmental impacts in feed efficient pigs dioxide (CO 2 ) during housing were calculated according to  Rigolot et al. (2010a and 2010b). Subtraction of N excretion and gaseous N lost in housing determined the quantity of N at the beginning of manure storage  (Garcia-Launay et al. , 2014). A sub-module was developed to estimate emissions, leaching and runoff during manure storage and application in the field.
Life cycle impact assessment An individual LCA was conducted for each pig in the LRFI and HRFI lines through incorporating its own experimental recorded traits and the traits obtained from InraPorc ® in the LCA model. The outputs of the LCA model were the impact categories of climate change (CC), terrestrial acidification potential (AP), freshwater eutrophication potential (EP), land occupation (LO) and water depletion (WD). For impact analyses, the ReCiPe Midpoint 2016 (H) V1. 13  (Huijbregts et al. , 2016) , one of the most recently updated life cycle impact assessment methods, with the Ecoalim  (Wilfart et al. , 2016)  and Ecoinvent  (Wernet et al. , 2016)  inventory databases, were used. The equivalency factors for the impact categories were assigned according to the factors recommended in the ReCiPe method.
Uncertainty analysis Monte Carlo simulations is an approach, available in SimaPro V8. 5. 4. 0, to quantify the effects of the uncertainties in the model parameters on the estimated environmental impacts: by resampling the parameter values based on assumptions about their uncertainties, a confidence interval for each impact can be obtained. In addition, the Ecoinvent LCA databases, which are embedded in SimaPro V8. 5. 4. 0, provide quantitative uncertainties for parameters in most of its processes, mainly with log normal distributions  (Ivanov et al. , 2019). To incorporate the intended traits in the LCA, a trait-based model was developed based on the growth performance equations presented by  van Milgen et al. (2008)  (also applied in InraPorc ® ) and linked to the emissions and excretions according to  Rigolot et al.
Acknowledgements This study is part of a European collaborative project ( SusPig ), financially supported by the  European Research Area on Sustainable Animal (ERA-NET SUSAN ) production (www. era-susan. eu)via the  French National Research Agency  contract  ANR-16-SUSN-0005. Data were collected in the framework of the  PIG_FEED  project funded by the  French National Research Agency   ANR-08-GENM-038. The authors are grateful to the staff of the experimental farm for data collection Environmental impacts in feed efficient pigs.
Evaluation of immunohistochemical data Tumoral and stromal cells were scored by a pathologist using the immuno-reactive-score (IRS) system. IRS system is the product of the staining intensity and the proportion of the positive stained tumor cells in comparison with negative tumor cells. Only cytoplasmic and or membranous staining were considered. Labeling intensity was scored from 0 to 3 as follow; 0: absence of staining, 1: weak staining, 2: moderate staining and 3: strong staining. The percentage of positive stained tumor cells was graded as follows: 0 for less than 10% of positive tumor cells, 0. 5 for 10-50%, and 1 for more than 50% of positive tumor cells. The final scores obtained were 0; 0. 5; 1; 1. 5; 2 and 3. According to this score, PDGFRα expression was classified into two categories: low expression (IRS = 0-0.
Samples features One hundred and three adenocarcinomas and 13 normal colon tissues were analyzed. The age of patients ranged from 22 to 88 years, with an average age of 57. 4 years (SD, 13. 2). There was a slight male predominance (63 male and 40 female) with a sex ratio equal to 1. 5. Most of the adenocarcinomas were at stage pT3 (54/99 cases (54. 5%) and left side located (49/98 cases (50 %). Histological study showed that the moderately differentiated adenocarcinoma was the most frequent histological subtype (64. 1%). Table  1  summarizes clinicopathological data of the study series.
2472C > T, c. 2481A > T, c. 2496G > A, c. 2514C > T, c. 2517G > T and c. 2520C > A). Except the c. 2481A > T, these variations were predicted to induce splicing site alteration by HSF tool (Table  3 ). In order to test if they change mRNA secondary structure, we used Mfold web server. All synonymous variations changed ss-count as compared to the full length and the partial reference sequence which might change the mRNA secondary structure except for the c. 2517G > T (data not mentioned). Partial mRNA folding structure of mutant compared to their wild type sequence were shown in Fig. 3. The mRNA folding carrying c. 2517G > T was similar to that of WT. Other synonymous variations may lead to the change of mRNA secondary structure.
The Close-up of mutations (seen from a slightly different angle) shows the protein in grey and the side chains of both the wild-type and the mutant residue in green and red respectively. Schematic structures show the original (green) and the mutant (red) amino acid. The backbone, which is the same for each amino acid, is colored red. The side chain, unique for each amino acid, is colored black Fig. 3  The effect of synonymous variations on mRNA secondary structure. Sanger sequencing chromatograms show the wild and mutated sequence of synonymous PDGFRA exon 18 variations (c. 2472C > T, c. 2481A > T, c. 2496G > A, c. 2514C > T, c. 2517G > T and c. 2520C > A). The effect of synonymous variations on partial mRNA (123pb) secondary structure is compared to the reference sequence at the same position (arrows) (COSM5772696) was described in colorectal cancer  [14] , as well as in other cancers  [30]  (COSMIC database: https ://cance r.
Recent data have demonstrated that EGF stimulates EGFR-PDGFRA transactivation and heterodimerization  [13]. The Fig. 4  generated by GeneMANIA bioinformatic analysis supported our hypothesis (https ://genem ania. org/). This figure showed EGFR-PDGFRA physical interaction collected from primary studies found in protein interaction databases, including BioGRID and Pathway Commons  [53]. Moreover, the EGFR and PDGFRα share large downstream signaling pathways as the activation of RAS/ MAPK pathway via various proteins (Fig. 5 ). These evidences suggest that protein overexpression or genetic variabilities identified in PDGFRA exon 18 could explain this resistance. By analyzing the PDGFRA exon 18 mutations, we have detected the c. 2464C > T mutation in 3 colorectal ADK cases all of which have WT RAS status.
brucei parasite cycle encountered in the tsetse fly vector are presented, as well as the two forms that can be grown in culture. Parasites were fixed with cold methanol and stained with DAPI (cyan) and the Mab25 antibody (magenta) that recognises an axoneme component  (Dacheux et al. , 2012). Flagellum length was measured using the Mab25 marker and is indicated for each stage. Data is from  Rotureau et al. (2011) , with the exception of dividing epimastigote cells that were specifically analysed in this study (n=37). Scale bar: 5 µm. microtubules (Fig. 2A, B ). Sections through the base of the flagellum revealed the flagellar pocket and its collar, from which the flagellum emerges at the cell surface (Fig. 2A, B ). Multiple flagellum sections were visible, with the presence of the canonical axoneme and of the associated paraflagellar rod (PFR).
Cells were maintained for 24 h in the presence of 10 mM teniposide, a drug that interferes with mitochondrial DNA segregation but not with basal body duplication nor with flagellum elongation  (Robinson and Gull, 1991). In these conditions, cells cannot divide anymore. This arrests cell division and the impact on flagellum length can be analysed by IFAs using DAPI to stain DNA and the axonemal marker Mab25 to measure flagellum length (Fig. 7 ). First, long trypomastigote parasites were Retrograde transport is more difficult to detect, and data were therefore only incorporated when the signal was sufficiently intense and reliable. (D) IFT particle frequencies (anterograde transport, magenta circles; retrograde transport, green squares) in the flagellum of short epimastigote cells and long trypomastigote cells were calculated and plotted according to the respective length of the flagella.
47g13 software (National Institutes of Health, Bethesda, MD). For presentation purposes and only after analysis, images were merged using Photoshop CS6 (Adobe). To quantify fluorescence intensity associated with the IFT signal (IFT22 or IFT172), the Mab25 signal was used to draw the ROI encompassing the length of the axoneme excluding the flagellum base  (Dacheux et al. , 2012). Using ImageJ, the total amount of IFT fluorescence was measured using the fluorescence associated with each pixel of the ROI and removing the background associated with the minimum of fluorescence detected in that ROI. Live imaging of parasites isolated from tsetse fly Fly dissections were performed in SDM-79 medium. The midgut, cardia and salivary glands were rapidly separated in different drops. After a visual inspection to confirm the presence of parasite, each organ was kept in 15 μl of SDM-79 medium supplemented with serum in Eppendorf tubes.
To increase parasite density, six organs were pooled in each tube and dilacerated. A SDM-79 solution containing 4% agar was heat-liquefied and progressively cooled for being mixed with the medium containing the freshly isolated parasites (1:1). For live video microscopy, the solution was spread on a slide, covered with a coverslip and rapidly observed at room temperature under the DMI4000 microscope. Videos were acquired using an Evolve 512 EMCCD Camera (Photometrics, Tucson, AZ) driven by the Metaview acquisition software (Molecular Probes, Sunnyvale, CA). IFT trafficking was recorded at 100 ms per frame during 30 s. Kymographs were extracted and analysed using Quia software as described previously  (Buisson et al. , 2013; Chenouard et al. , 2010).
Acknowledgements We thank  Aline Crouzols  and  Christelle Travailléfor  help with tsetse fly infection and dissection;  Moara Lemos  for help with sample preparation and  Derrick Robinson  ( University of Bordeaux ) for providing the Mab25 antibody. We are grateful to the  Photonic Bioimaging and Ultrastructural Bioimaging  facilities for access to their equipment, and to  Geŕard Pehaud-Arnaudet  for training in electron microscopy.
0 arrays. This analysis excluded linkage of rare copy number variants, and led to the identification of two possible loss of heterozygosity (LOH) regions at chromosome 2q21. 3 and 22q13. 1-13. 2. The LOH region at chromosome 2q21. 3 was excluded by short tandem repeat (STR) marker analysis. The LOH region at 22q13. 1-13. 2 was further confirmed and fine mapped using STR markers and single-nucleotide polymorphisms rs9306345 and rs2038062 (Supplementary Fig. 1a ). Statistical analysis of haplotypes using FASTLINK software calculated a maximum LOD score Z = 8. 61, at allele frequency (p = 0. 01), and disease frequency of 0. 001. The resultant 2. 5 Mb homozygous region at chromosome 22 (40,436,371-43,001,960 bp, hg38) contained 49 annotated protein coding genes. Owing to the large number of genes within the linkage region, we next performed whole exome sequencing (WES) using genomic DNA obtained from one affected individual.
Location of sections is indicated by red dotted lines in b (upper panel). Note the reduction in cells in the pallium (p) of rrp7a -/-compared with rrp7 +/+ (marked in zoomed box, and quantified at right, n = 3, biologically independent animals). Data are presented as mean ± SD (P = 0. 007). h hypothalamus, le lens, mc Meckel´s cartilage, mo medulla oblongata, ot optic tectum, sp subpallum. Scale bars, 0. 1 mm. h mRNA rescue of rrp7a mutants. Normalized eye diameter and lateral head size were quantified and plotted as box and whiskers, whiskers min to max. i Left: northern blot (top) of total RNA from pooled zebrafish larvae analyzed with 18S rRNA and 28S rRNA probes, respectively, and qRT-PCR (bottom) displaying relative 18S/28S rRNA ratios with wt set to 1 (data average of n = 5 independent experiments).
Magnetic resonance imaging. Magnetic resonance imaging (MRI) scan was performed for one affected individual at the Sheikh Zayed Hospital, Rahim Yar Khan. Multiplanar, multisequential FLAIR and diffusion images of brain MRI were obtained in axial, coronal and sagittal views and images were analyzed using syngo fastView (version 1. 0. 0. 3, Siemens AG). Genome-wide SNP array mapping and linkage analysis. To identify regions of homozygosity, deletions, or duplications, samples were genotyped on Genome-Wide Human SNP array 6. 0 (Affymetrix Inc. , Santa Clara, CA, USA). The analysis was performed in four patients (one from each loop) according to the manufacturer's standard protocol (Affymetrix). Data analysis was performed using Chromosomal Analysis Suite (ChAS) and FASTLINK software tools  64.