dataesr/scientific-paragraphs-categorization huggingface dataset:
Mice were sacrificed 96 h after beginning of dox-treatment, and tumors were collected for RNA and histological analysis. Total RNA was extracted using the ReliaPrep miRNA Cell and Tissue Miniprep System (Promega). Knock down of EWSR1-FLI1 was confirmed by qRT-PCR as described  23  , and proved to be downregulated onto 15% of the control (sucrose only). Routine histology (H&E stains) of the tumors confirmed high tumor purity (>95%). The transcriptomes of 3 dox(+) and 3 dox (-) were profiled on Affymetrix Clariom D arrays (RIN > 9). Microarray data were simultaneously normalized on gene level using Signal Space Transformation Robust Multi-Chip Average (SST-RMA) and Affymetrix CDF. Animal experiments were conducted in accordance with the recommendations of the European Community (86/609/EEC), the Government of Upper Bavaria (Germany), and UKCCCR (guidelines for the welfare and use of animals in cancer research).
Acknowledgements This work was supported by the  Intramural Research Program of the U. S. National Cancer Institute  and the  Intramural Research Program of the American Cancer Society. This work was supported by grants from the  Institut Curie , the  Inserm , the  Ligue Nationale Contre le Cancer (Equipe labellisée ,  Carte d'Identité des Tumeurs program and Recherche Epidémiologique 2009 program ), the  ANR-10-EQPX-03  from the  Agence Nationale de la Recherche , the European PROVABES (ERA-649 NET TRANSCAN JTC-.
Bioinformatic analysis A stepwise bioinformatic filtering pipeline was used to quality filter reads using cutadapt and sickle; and to remove human, archaeal, bacterial, and fungal sequences by aligning reads with bwa mem. The databases used were GRCh38. p2, RefSeq archaea, RefSeq bacteria, and RefSeq fungi. Remaining reads were aligned on ezVIR viral database v0. 1  [22]  and bacteriophage genomes from the RefSeq database (downloaded on 17 February 2017) using bwa mem. Normalization for comparing viral genome coverage values was performed using reads per kilobase of virus reference sequence per million mapped reads (RPKM) ratio  [4, 23]. RPKM ratio corrects differences in both sample sequencing depth and viral gene length. Viral reads (expressed in RPKM) from the No-Template Control (NTC) were subtracted from viral reads (in RPKM) of each sample within the batch prior to further analysis.
1 ). QCT1 validation criteria were: MS2 semi-quantitative PCR Cycle threshold (Ct) below 37 Ct for IQC and EQC, and no MS2 detection for NTC. QCT2 evaluated the sequencing performance by quantifying the number of reads aligned on the MS2 genome (in RPKM) and MS2 genome coverage (MS2 genome accession number: NC_001417. 2; Fig. 1 ). QCT2 validation criteria were MS2 genome coverage > 95% for positive EQC, and an MS2 RPKM > 0 for IQC.
Acknowledgments We thank  Audrey Guichard ,  Gwendolyne Burfin ,  Delphine Falcon  and  Cecile Darley  for their technical assistance as well as  Philip Robinson  ( DRCI, Hospices Civils de Lyon ) for his excellent help in manuscript preparation. Part of these data has been presented at the  International Conference of Clinical Metagenomic held in Geneva in October 2017.
Some studies have even found seasonal adaptations of the digestive system of snakes as a function of prey-resource availability  (Santos and Llorente, 2008). This feeding regime is possible, not only because of their ability to ingest large prey but also because metabolic rate can fall in poikilothermic animals. Food-intake rate in reptiles can be estimated from information on daily energy expenditure. Fryday and Thompson (2009)  compiled the information available for 67 species, and calculated allometric equations relating body mass and daily energy expenditure. Then, considering the diet composition for the different species (i. e. arthropods and soil invertebrates for small lacertids to small vertebrates for snakes) and the energy and relative moisture content in each prey type, food-intake rate can be estimated, although no empirical data have been collected to validate such estimations.
Inventory of species The next step is to have an inventory of EU amphibians and reptiles. An updated list was created using the database of the IUCN red list (http://www. iucnredlist. org), completed with the catalogue of species included in the Atlas of Amphibians and Reptiles of Europe developed by the European Herpetological Society  (Sillero et al. , 2014 ; atlas available at http://na2re. ismai. pt/). The databases of  Frost (2017)  and  Uetz et al. (2016)  were used as reference for standardisation of nomenclature of amphibians and reptiles, respectively. For simplification, only species native to the EU territory and excluding overseas areas (i. e. Macaronesia, northern African and Trans-Oceanic territories) have been included. The list of species can be seen in Appendix A, which includes also information on distribution, namely with the presence in each of the three zones defined for pesticide risk assessment according to the Regulation 1107/2009, as well as their taxonomical classification.
Model overview The model is an agent-based simulation model working at landscape scale (here taken as 10 9 10 km). It represents individual newts as eggs, larvae, juveniles and adults. Eggs and larvae are aquatic in ponds, juveniles are terrestrial, whist males and females are primarily terrestrial except for breeding periods. The model itself was written in C++ and is part of the ALMaSS simulation system. The code and model are documented using ODdox protocol  (Topping et al. , 2010a,b) , and the documentation is available at https://almassdocs. au. dk/ALMaSSODdox/Newt/index. html.
While refinement of individual exposures is possible, for population-level effects it is critical that all stages and exposure routes are taken into account concurrently if realistic effects are to be predicted. One key aspect of the scenarios that needs to be considered for long-term population models is the weather input. The newt model is highly sensitive to weather data. Figure  14  shows the data from the LC x experiment but with additional points created by running LC x scenarios under weather from 1950 to 1980 instead of the last 30 years' weather. The weather clearly has a large impact on the vulnerability of the population to pesticide effects; therefore, a careful consideration of weather is needed in any regulatory scenario. These newt model scenarios did not utilise all possible features of the ALMaSS simulation system.
4. 9. Conclusions and recommendations 4. 9. 1. Conclusions Population structure and spatio-temporal dynamics can have important implications for the evaluation of impacts of pesticide on amphibian and reptile populations. A systems approach is therefore recommended, for inclusion of both spatial and temporal implications of pesticide usage and to take the ecological state of the population before application of pesticides into account (EFSA Scientific Committee, 2016a). Spatially explicit, individual-based modelling at landscape scales is an important part of the ERA toolbox for amphibians and reptiles. It should be used to help set the tolerable magnitude of effects for SPG, to translate toxicity data into population-modelling endpoints, and as a higher tier assessment tool. Precise context for application of the models requires careful consideration.
General considerations Regulation (EC)  No 1107 /2009 (European Commission, 2009)  defines general protection goals that aim at protecting inter alia non-target organisms, biodiversity and ecosystems. It is thus necessary to define specific protection goals (SPGs) aiming at implementing this general protection into explicit and viable mandates for risk assessors, who need to know what to protect, where to protect it and over what time period. Final decisions on the choice of specific protection goals need to be made in consultation with risk managers. The role of EFSA's risk assessment is to propose possible SPG options based on environmental and ecological criteria (and related exposure assessment goals), acknowledging existing general protection goals described in the relevant EU Regulation or Directive and regulatory data requirements.
, 2007; Verro et al. , 2009). Consequently, when addressing cumulative stress of pesticides in ERA, it seems cost-effective to focus on those pesticides that dominate the exposure in terms of toxic units in the relevant medium (e. g. > 90%). It is important, however, that a range of scenarios altering potential vulnerability of populations is taken into account (e. g. highly stressed populations may be more vulnerable to further stressors). Information on the distribution of crops in agricultural landscapes and frequently occurring pesticide combinations may be derived from existing databases (e. g. databases under the EU subsidies scheme and databases from EU pesticide usage as collected within the frame of the Sustainable Use Directive;  Garthwaite et al. , 2015). This information may provide important inputs for population models to evaluate effect periods and recovery times following pesticide stress in a realistic, agricultural landscape context (e.
The survey made an inventory of all ponds (as defined above), including ponds where amphibians may not be present. It covered a total of 591 1 9 1 km square samples spread across England, Scotland and Wales. The ponds may be isolated or not. The survey demonstrated that almost two-thirds (63%) of ponds were directly linked to the stream network and that a third of these ponds had an inflow but no outflow, suggesting that many ponds intercept and retain drainage water. The database contained 259 ponds but water surface area was measured only for 257 ponds, mostly in the period May-October 2007. • 23% of the 257 ponds with measured water surface had a surface area of 100 m 2 or less (the surface area of FOCUS ditches and streams) • 79% of the 257 ponds with measured water surface had a surface area of 900 m 2 or less (the surface area of the FOCUS ponds).
They conducted an extensive review using a species-sensitivity-distribution (SSD) approach and analysed the median lethal concentration (LC 50 24-96 h) data from almost 24,000 studies with aquatic species retrieved from the AQUIRE database of the USEPA to compare the sensitivity of 44 amphibian species with that of other groups of organisms. They concluded that amphibians (eggs and larvae) are of low to moderate sensitivity to metals, inorganic chemicals, and pesticidal active substances (pyrethroids, carbamates, organophosphates or organochlorines) when compared with 13 other classes of organisms, including fishes. The estimated acute hazard concentration values (HC 50 values, as usually derived from SSD) were above the average estimates for all taxa analysed signifying an overall low relative acute sensitivity.
Here, the data were extended with endpoints for aquatic plants (Figure  30 ). The data for amphibians were obtained through literature search and taken from the databases PAN Pesticide and ECOTOX and cross-referenced with the original literature. The data for the surrogate species were taken from the EFSA conclusions on individual active substances. The majority of data was found for herbicides and insecticides and 24 substances from 14 different chemical groups could be compared. In 11 cases aquatic invertebrates reacted most sensitively, in two cases fish, in 10 cases aquatic plants and in one amphibians were most sensitive (dimethoate) (see Figure  30 ). For dimethoate, the variability in the published endpoints for amphibians was rather large. A review  (Weltje et al. , 2013)  compared acute and chronic toxicity data obtained from the U.
S. Environmental Protection Agency (USEPA) ECOTOX database supplemented with data from the scientific and regulatory literature. Acute data were collected for amphibian species and either rainbow trout (Oncorhynchus mykiss) or fathead minnow (Pimephales promelas) (Figure  31 ). Only tests from ECOTOX that reported measured concentrations of test chemical were included. A geometric mean value was calculated if more than one 96-h LC 50 value was available for the same chemical and species. When data on several amphibian species were available, only data for the most sensitive amphibian species were selected for further analysis. The comparison was based on 55 chemicals (eight inorganic chemicals and 47 organic chemicals, of which 32 were pesticidally active from 13 different chemical groups).
The LC 50 data for the tadpoles were determined after 8-day exposure in a semistatic test based on nominal concentrations using commercial formulations  (Ghose et al. , 2014). The 96 h LC 50 values for fish were collected from the Pesticide Properties DataBase or USEPA REDs. The comparison for the ten active substances indicated higher sensitivity of fish for eight substances. The apparently higher sensitivity for glyphosate and paraquat of A. callidryas was possibly attributed to the longer exposure time, increased toxicity due to the formulation or unconfirmed test concentrations. The study authors conclude that fish are suitable surrogates to cover the sensitivity of aquatic stages of a tropical amphibian species and that the standard assessment factors cover the extrapolation to potentially more sensitive species and other uncertainties.
If the assessment factor of 100 used for the acute risk assessment of fish is applied to cover the variability between species, then in 2% of the cases the amphibian test species is more than a factor of 100 more sensitive than the rainbow trout (values below the red line in Figure  32 ). In those cases, the LC50 for amphibians would be lower than the RAC based on the rainbow trout. Repeating this analysis but splitting it by life stage (i. e. keeping embryos and larvae separate) gave a comparable result to the assessment on the whole data set. Therefore, the results were considered to be valid for both embryos and larvae. The dotted line delineates a factor of 100, which is the standard EU assessment factor applied to fish acute toxicity data for plant protection products. (Weltje et al. , 2013)  Based on the same LC 50 data for amphibians collected by  Fryday and Thompson (2012)  and using the acute LC 50 for fish listed in the Pesticide Properties DataBase (PPDB database) supplemented by LC 50 for thirteen older active substances listed in the aquatic guidance document (EFSA, 2013), the correlation is further investigated here.
Hence, the data from  Fryday and Thompson (2012)  were extended with endpoints obtained from the Pesticide Properties DataBase (PPDB database) for aquatic invertebrates and aquatic plants; thus 72 substances could be compared. In nine cases (14%), the lowest endpoint for amphibians was lower than the lowest endpoint of the current surrogate species. When plotted against the currently used RACs (by including the current assessment factors (AF) of 100 for fish and aquatic invertebrates and 10 for aquatic plants), the data indicate that all acute toxicity endpoints (LC50) for amphibians are above those RACs (Figure  35 ). However, in the context of risk assessment, AF to calculate RACs need to consider all relevant uncertainties. Besides covering the species variability in toxicity, also uncertainties with regard to i.
A sensitivity distribution between different amphibian species spanning several orders of magnitude was also noted by  Ghose et al. (2014). Chronic toxicity data for fish were available for 52 chemicals (10 inorganic chemicals and 42 organic chemicals, of which 20 were pesticidally active substances)  (Weltje et al. , 2013). Chronic toxicity data were taken from USEPA databases and scientific literature, for fish also from EU or US regulatory dossiers. Data were retained for analysis if they were from studies of at least a 10-day duration for amphibians and 21-day duration for fish, employed either static renewal or flow-through aqueous exposure-study designs, and reported apical endpoints of potential population relevance (i. e. they were related to survival, growth, development (including metamorphosis), or reproduction).
The databases for Spain and the Aargau canton in Switzerland contain only water bodies in which amphibians have been observed, while the database for the UK is much larger. Below, we present an analysis of the information contained in these databases with respect to (i) water surface area, water depth and water volume, and (ii) surrounding land use. The aim of this analysis was to establish sizes of ponds (and other water bodies) that serve or may serve as aquatic habitat for amphibians and in addition, establish whether ponds (or other water bodies) that serve as aquatic habitats for amphibians, are associated to agricultural land use in their surroundings. In this way, we are able to evaluate whether the amphibians are likely to be exposed to pesticides residues in their aquatic habitat.
Spain The database of the Spanish ponds was built with the data of the program SARE (monitoring of Spanish amphibians and reptiles) developed by the Spanish Herpetological Society. Details on the program can be found at http://siare. herpetologica. es/sare (available in Spanish only). The Spanish Herpetological Society kindly provided the entire database of ponds. The SARE monitoring is carried out by volunteers who select one or several cells of the 10 9 10 km UTM grid. Within each cell, the person responsible for the monitoring must design the sampling, as part of which at least three water points have to be selected for sampling aquatic amphibians. When designing the monitoring, the volunteers record some information about the water points, including dimensions like the surface area (for ponds, for which surface shape is assumed to be elliptical, lengths of the major and minor axes are collected) and maximum depth of the water column, and characteristics of the surrounding habitat.
g. a water body has desiccated). It also may happen that significant changes are observed in the characteristics of the water bodies, which means that the dimensions have to be recorded again for the same point. For this reason, the same pond may appear more than once in the database. The first data of the program were recorded in 2010. More data are available for the first years of the program (2010 and 2011) because ponds are characterised when volunteers incorporate to the program, and this happened mostly during the initial years; afterwards, the number of volunteers contributing to the program dropped. The database of ponds sampled as part of the SARE program provides a very good overview of amphibian breeding habitats. Contrarily to other pond inventories, these ponds are included here precisely because they constitute amphibian breeding habitats, and with such purpose have been selected by volunteer herpetologists (either amateur or professional) who have a good knowledge on the habitats they are sampling.
This was done to be able to evaluate whether the ponds were liable to be contaminated by the agricultural use of pesticides. The SARE database contained 794 unique records for water bodies. The water bodies have been classified as ponds (421), artificial pool (152), dam/reservoir (66), lagoon/lake (21), river (30), stream (85) and wetland/marsh (19). Water depth, water surface area and water volume were analysed of all water bodies. First, the database was corrected, as some water depths (e. g. 100 m) were not plausible. In records of ponds, artificial pools, and streams in which the water depth was more than 10 m and the minor axis was less than or equal to 10 m, it was assumed that the depth was reported in cm instead of m, and the depth was converted to m. This was done for 23 ponds.
Land use There are no data for the Swiss canton Aargau ponds that specify the land use in their immediate surroundings. So, we are not able to evaluate on a quantitative basis whether the amphibians in these ponds are likely to be exposed to pesticides residues in their aquatic habitat. However, the Aargau canton being a canton with intensive agriculture and a good distribution of amphibian populations, a number of the ponds in the database are likely to represent amphibian habitats that may receive pesticide residues. In the United Kingdom, the current state of ponds was described by the Countryside Survey of 2007  (Williams et al. , 2010). The survey covered a total of 591 1 9 1 km 2 samples spread across England, Scotland and Wales. A pond was defined as a body of standing water of 25 m 2 to 2 ha, in area, which usually holds water for at least 4 months of the year.
The database from CountrySide Survey (2007) contains 259 records with ponds for which the water surface area has been measured (POND_WATER_QUALITY csv file). Water depth has been recorded, water bodies in canton Aargau, Switzerland. Note that in graph 4A the x-axis is not linear and that all class boundaries are indicated. In graph 4B, water bodies with a volume greater than 900 m 3 have not been included but only for a limited number of ponds as the time and equipment lacked to measure the maximum depth in ponds, where it was too deep to wade (pers. comm. J. Biggs, 11 Nov 2016).
Since no specific enzyme-linked immunosorbent assay (ELISA) is available for SOV, based on the close genetic relationship between PVM and SOV Hause et al. used a commercial ELISA to detect antibodies to PVM and found that 31% of the analysed feral swine sera were antibody positive  [4]. Finally, analyses by the same PVM ELISA of sera from different American farms revealed that sera were 33% to 93% positive, dependent on the farms, and confirmed that SOV is also present in domestic swine. Interestingly, using bovine respiratory syncytial virus antigens, in 1998 Allan et al. found that 41% of pigs sera from 61 herds in Northern Ireland were reactive with BRSV antigens  [5]. Although  SOV  has not yet been isolated, the complete genomic sequence of SOV (strain 57) is available (Gen-Bank accession number: KX364383.
Expression and purification of recombinant SOV nucleoprotein The full-length SOV N and P coding sequences (Gen-Bank accession number: KX364383. 1) were synthesized for optimized expression in Escherichia coli (Genscript). Our previous studies with respiratory syncytial virus showed that the P C-terminal disordered region (PCT, amino acid residues 161-241, Figure  1 ) fused to GST is very efficient for purifying HRSV N protein after co-expression in E. coli  [6]. The same approach was applied to SOV. First, the C-terminal disordered region of SOV P (amino acid residues 208-295) was determined by alignment with HRSV P (Figure  1 ). The identified region of SOV P was amplified by PCR and subcloned in pGEX-4T-3 at BamHI-XhoI sites to engineer the pGEX-PCT vector. SOV N gene was subcloned in pET28a+ at NdeI-XhoI sites to engineer the pET-N vector.
Learn more biomedcentral. com/submissions Ready to submit your research ? Choose BMC and benefit from: Santé et Agronomie (IBISA). FM is supported by an establishment grant from the Région Pays de la Loire (RFI Food for tomorrow-Cap aliment). This lab work was carried out with the financial support of the regional programme "Food for Tomorrow/Cap Aliment; Research, Education and Innovation in Pays de la Loire", which is supported by the French Region Pays de la Loire and the European Regional Development Fund (FEDER). Thanks also to Prof. Alain Chauvin and Daniel Hogan for their helpful comments.
Acknowledgements We acknowledge the usage of the electron microscope of the dedicated platform of the Grenoble Instruct center (ISBG;  UMS 3518 CNRS-CEA-UGA-EMBL ) with support from  FRISBI [Agence Nationale de la Recherche (ANR) ] ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB). The electron microscope facility is supported by the  Rhône-Alpes Region , the  Fondation pour la Recherche Médicale (FRM) , the fonds  FEDER , the  Centre National de la Recherche Scientifique (CNRS) , the  Commissariat à l'Énergie Atomique et aux Energies Alternatives (CEA) , the  University of Grenoble Alpes ,  EMBL , and the  GIS-Infrastructures en Biologie.