dataesr/scientific-paragraphs-categorization huggingface dataset:
Eggs were staged according to  Nieuwkoop and Faber (1994). After reaching stage 46, normally cleaving embryos were manually collected from the tank with a plastic dropper and placed in sterile plastic Petri dishes for the exposure to the mixtures or SC, in five replicates, each containing 30 embryos in 10 ml of solution. Solutions were changed every 24 h, and dead embryos were removed. After 96 h, embryos from each dish were moved to test tubes and anesthetized with 5 ml of 100mg/l tricainemethanesulfonate, and then fixed with 5 ml of 3% formaldehyde. The embryos were observed with a light microscope, digitally photographed, and measured with QuickPhoto MICRO software. The parameters evaluated in this test included mortality, embryo length, and the number and type of malformations and were assessed according to the Atlas of Abnormalities  (Bantle, 1991).
After 24 h of incubation (37 • C, 5% CO 2 ), the medium was replaced by medium containing the water extracts (1% DMSO). A dose-dependent response ranging from 1× to 1000×EQS was investigated for Mix14 and from 1× to 100×EQS for Mix19. After 24 h of incubation, the medium was removed and the cells were lysed in 30 l of Triton-lysis buffer. The amount of luciferase activity was quantified using a luminometer (MicroLumat Plus, Berthold Technologies, Switzerland). All plates included a doseresponse curve of the reference compound 17␤-estradiol. All mixtures and estradiol were analyzed in triplicates. Only test concentrations where no cytotoxicity was observed using a microscope were used for quantification of the response (  Van der Linden et al. , 2008). The data were evaluated by fitting a doseresponse using GraphPad Prism 5 Software (La Jolla, California, USA).
YES assay. The YES was performed according to  Routledge and Sumpter (1996)  with recombinant yeast Saccharomyces cerevisiae provided by John Sumpter (Brunel University, Uxbridge, UK). At test initiation, 1:2 dilution series of the reference substance 17␤estradiol, the mixtures, and SC (ethanol) were pipetted into triplicate wells on 96-well plates and the solvent was evaporated completely under sterile conditions. Suspension with 4 × 10 7 yeast cells was seeded on the test plate (200 l/well) and incubated at 30 • C. After 72 h, cell density (OD 620 nm ) and color change (OD 540 nm ) were measured using a plate reader (Synergy 2, Biotek). A dose-dependent response ranging from 0. 8× to 1000×EQS was investigated for Mix14 and from 0. 8× to 100×EQS for Mix19. The data were fitted to a sigmoidal curve with Graph-Pad Prism 5 Software (La Jolla, CA, USA).
In vitro Human ER␣ Competition Assay To test the binding ability of the recombinant receptor we used the PolarScreen ER␣ competitor green assay developed by Life Technologies, with a recombinant wild-type ER␣ ligand binding domain (wtER␣ LBD )  (Ferrero et al. , 2014). The assay is based on the displacement of the Fluormone ES2 from the ER receptor by competitor molecules and a consequent decrease in the maximum fluorescence signal. The intensity of the fluorescence polarization (P) signal was measured with an Infinite 200 Pro multimode plate reader (Tecan). A dose-dependent response ranging from 0. 01× to 200×EQS was investigated for Mix14 and from 0. 001× to 20×EQS for Mix19. 17␤-estradiol was used as a reference compound. The data were fitted to a sigmoidal one site competition four parameters logistic curve with OriginPro Software.
Zebrafish Embryo Estrogenic Activity Assay The estrogenic potency of the mixtures was assessed by the in vivo test EASZY (Detection of Endocrine Active Substances acting through human ER, using transgenic cyp19a1b-GFP zebrafish embryos)  (Brion et al. , 2012). Newly fertilized zebrafish eggs were exposed to the mixtures for 96 h under static condition. A range of three dilutions was tested, from 0. 04× to 4×EQS for Mix14 and from 0. 04× to 0. 4×EQS for Mix19, with 17␣-ethinylestradiol (EE2) (0. 05nM) as a reference compound. Three independent experiments were performed. At the end of each experiment, the fluorescence of each living zebrafish embryo was acquired using a fluorescence microscope and quantified using ImageJ. The data (expressed as mean fold induction above control) were analyzed to determine the estrogenic activity of each mixture using a parametric two-way ANOVA and post-hoc test using R statistical software.
NGM agar plates (20-ml volume) were inoculated with 200 l of the spiked OP50 and the seeded plates were incubated at room temperature for 24 h (to allow for bacterial growth). All strains were aged synchronized, placed (as L1 larvae) on the NGM plates and exposed to the respective conditions for 48 h at 20 • C. Single worms were picked onto a glass slide with a drop of M9 and immobilized with sodium azide (2%). Images were captured with a Nikon DS-2Mv digital camera and NIS-Elements F 2. 20 software linked to a Nikon ECLIPSE TE2000-S-inverted microscope, using the filters G-2A (Ex 510nm-560nm) for mCherry and FITC (Ex 465nm-495nm) for GFP. The fluorescence intensities from 10 worms per condition were analyzed using ImageJ. For the growth size assay, wild-type nematodes (N = 10 per condition) were plated on NGM plates (containing the OP50 medium with the mixtures) and maintained up to 120 h.
Adult nematodes were transferred to new plates between 72 h and 96 h to remove hatched offspring. Images of worms were obtained using an inverted microscope and the flat volumetric surface area and length determined by tracing the nematodes using the Image-Pro Express software (Media Cybernetics, Inc. ). Data obtained from the fluorescence experiments were analyzed using the one-way ANOVA followed by the Tukey's multiple comparison test for significant differences between the treatments. The phenotypic assays were assessed by means of the two-way ANOVA. All tests were executed with GraphPad Prism.
The primers used and the genes they are directed against are listed in Supplementary table 3. These included androgen receptor (AR), ER␣, ER beta (ER␤), metallothionein (MT2A), cytochrome P450, family 1 subfamily A, polypeptide 1 (CYP1A1), glutathione S-transferase, cyclooxygenase-2 (COX2), interleukin-6 (IL6), interleukin-8 (IL8), and tumor suppressor protein (p53). Data variance were analyzed using the GraphPad Prism 5 software by one-way (ANOVA) followed by Dunnet post-test for multiple group comparison.
ACKNOWLEDGMENTS Authors from JRC thank  Ramon Lavado ,  Joaquin Pinto-Grande , and  Simona Tavazzi  for technical assistance and  Giovanni Bidoglio  and  Bernd Manfred Gawlik  for their encouragement. Authors from RECETOX acknowledge technical assistance of  Alena Nekvapilov  á and  Mirco Dalla Bona. King's College London thanks  Dr Chris Hopkins  ( Knudra Transgenics ) for the generous provision of transgenic C. elegans stains. Daniela Baumgartner  and  Dr Marion Junghans  at the  Swiss Centre for Applied Ecotoxicology  are thanked for technical assistance and data analysis. Competing financial interests declaration: R. G. V. is cofounder of  Evercyte GmbH  and declares conflict of interest. All remaining authors declare they have no competing financial interests.
e. changes in the relative abundance of species)  (Pauly et al. 2000). Nevertheless, although ontogenetic effects are likely to be small in catch-based indicators that only account for recruited stages, they may be important to consider in community indicators such as those based on survey data. Model-based TL indicators that capture ontogenetic change are better equipped to handle ontogenetic changes in TL, since these changes are defined within the model. Below, we describe a suite of TL-based indicators, often used to quantify fishing effects on the trophic structure of food webs, and summarise the processes leading to change in the indicators. We then interpret trends in indicators using catch, survey and modelled data for multiple ecosystems in relation to fishing pressure and fisheries management.
However, TLs based on modelled communities are dependent on a well understood predation process, grounded in reliable diet data. Further, these models should be fitted to data and validated with external observations and knowledge (e. g. Shannon et al. 2004 , Coll et al. 2008 , Travers-Trolet et al. 2014) , thus minimising the uncertainty associated with these tools. TL-based indicators (TL IND ) are calculated as the mean trophic position of all species, weighted by the relative biomass of each species in the landings, in the surveys or in the modelled community: (1) where TL i is the trophic level of species i, X L is total landings (for TL of the landings) or total biomass (for TL of surveyed or modelled ecosystems) and X i is the landings or biomass of species i. See Table  2  for further details.
Analyses across ecosystems For 9 case studies (see Supplement 1, Fig. S1  and Table  S1  at www. int-res. com/articles/suppl/ m512 p115 _supp. pdf) included in the IndiSeas project  (Shin et al. 2012) , trajectories of TL-based indicators were calculated from 1980 to 2010 (or shorter time series due to data availability) using landings data, survey data and modelled data. Nine TL-based indicators were calculated: (1) TL L , (2) MTI 3. 25 , (3) MTI 4. 0 , (4) TL SC , (5) TL SC3. 25 , (6) TL SC4. 0 , (7) TL MC , (8) TL MC3. 25 and (9) TL MC4. 0 (Table  2 ). For ease of comparison between ecosystems, all indicators were normalized following  Blanchard et al. (2010). Data from catches and surveys were obtained by experts from the 9 ecosystems, and indicators were calculated using local datasets, as explained in  Shin et al.
(2010). To calculate TL-based indicators with different cut-offs, TLs by species or groups were obtained from local studies, modelling activities or, when unavailable (e. g. for specific species that were aggregated into a single model group), from Fish-Base (http://fishbase. org) and SeaLifeBase (http:// sealifebase. org). TLs adopted per species or species group per ecosystem are provided in Tables  S2  & S3  in Supplement 2 at www. int-res. com/articles/suppl/ m512p115_supp. pdf. Ecosystem models used to calculate indicators were parameterized using the Ecopath with Ecosim (EwE) software and approach  (Christensen & Walters 2004). The 9 EwE models used in the study had been fitted to time series, previously validated, and were already well documented (see Supplement 1 and Table  S1 ). The trophic models did not necessarily need to be fit to the most recent years, but selection was constrained to models for which the fitted timeseries overlapped well with the catch-based and survey-based TL indicator series.
Across all ecosystems, we examined how the TL-based indicators were correlated using all available data for these ecosystems spanning the period 1980 to 2010. This analysis was done assuming the same weight for all ecosystems after TL-based indicators per system had been calculated. The relationship between the TL-based indicators and an indicator of fishing pressure (the ratio of landings to survey biomass) was also investigated using correlation analysis. This indicator of fishing pressure was selected because it is the simplest and most readily available indicator across the ecosystems examined  (Shin et al. 2010). Relationships were also evaluated between TL-based indicators and modelled fishing effort and/or fishing mortality (when available, from the models fit to data), and with total landings.
In addition, we used correlation analysis to explore whether the set of 9 TL-based indicators may be influenced by environmental drivers such as sea surface temperature (SST) and productivity (i. e. chlorophyll a; chl a) for each of the 9 ecosystems. These environmental data were extracted for ecosystem study areas (H. Demarq pers. comm. ); SST indicators were extracted from AVHRR/Pathfinder and MODIS, and chl a from SeaWiFS and MODIS. All correlations were evaluated using Spearman's non-parametric rank order correlation coefficient  (Spearman 1904) , which is a measure of statistical dependence between 2 variables, ranging between -1 and 1 (perfect negative and positive correlation, respectively). The test assesses how well the relationship between 2 variables can be described using a non-linear monotonic function.
2) and herring C. harengus (TL = 3. 8), albeit with un known catchability. The difference between TL MC3. 25 and TL SC3. 25 was due to a high abundance of sandeels in the model: removing sandeels from the dataset increased the correlation between indicators from 0. 27 (p = 0. 29) to 0. 62 (p < 0. 01). A non-significant correlation was found between TL MC4. 0 and TL SC4. 0. Much of the difference here was due to whiting Merlangius merlangus (TL > 4), since they were the dominant group by catch-rate in the survey but not in the model: for example, the catch-rate of whiting in the survey was 4 times that of cod despite similar average biomasses for these species in the model. Despite a good correlation between the model biomass and the survey catch rate for this species (R = 0. 69, df = 15, p < 0.
01), simply by removing whiting from the dataset, the correlation between the indicators in creased from 0. 34 (p = 0. 19) to 0. 60 (p = 0. 01). Hence, differences between indicators could merely have been a result of different data treatment, and thus relative weighting of species within the dataset, rather than differences in underlying state. In the case of the West Coast of Scotland, surveys suggested a degraded ecosystem whereas models were reflecting increases in other species (e. g. Nephrops norvegicus and top predators) and possibly even that there has been a regime shift in this ecosystem, specifically in the Clyde Sea (a subsection of the West Coast; Thurstan & Roberts 2010,  Howarth et al. 2013). Despite non-significant correlations between TL L and survey-and model-based TL indicators on the West Coast of Scotland (Table  4 ), survey-based TL indicators declined over time for TL SC and TL SC3.
Thus, comparisons across ecosystems, large marine ecosystems or regions must be carefully drawn when different datasets are combined. In this study, we have explored the use of different data sources to calculate TL-based indicators with 3 cut-off points, and contrary to  Branch et al. (2010) , we conclude that TL-based indicators are useful -especially when all 3 data sources and cut-off points are used, which together can shape a more complete understanding of the effects of fishing on the trophic structure of ecosystems. However, TL-based indicators, as with any indicator, need to be interpreted within regional or local information. This study has provided new information about the utility of TLbased indicators, and specifically recommends that TL-based indicators should not be used without contextual information about the history of exploitation in the fishery and fundamental knowledge of ecological changes and environmental drivers.
g. upwelling systems), comparing trends in TL L is recommended in addition to MTI 3. 25. Further, TL SC provides a fuller picture of what is happening at the community level and may capture combined effects of fishing and the environment more clearly (and possibly with less of a time lag) than TL L or MTI 3. 25. In addition and when available, TL MC indicators should be included in the analysis to verify the inclusion of essential ecosystem processes in TL L and TL SC indicators; (8) All 3 types of TL indicators (catch-, survey-and model-based) provide information that is useful for ecosystem-based fisheries management. collaborative project MEECE -Marine Ecosystem Evolution in a Changing Environment -(FP7, contract n°212085). A. B. was supported by Fisheries and Oceans Canada SPERA project 'Ecosystem indicators for ecosystem monitoring at different scales' (www.
dfo-mpo. gc. ca/science/ecosystem/ projects/detail-eng. asp?pid=21). K. K. was supported by Conservation International and the 'Sea Around Us' project, a collaboration between The University of British Columbia and The Pew Charitable Trusts. C. P. L. and C. P. were supported by the EU FP7 project DEVOTES (grant agreement no. 308392), www. devotes-project. eu. C. P. L. was also supported by Defra M1228. This is a contribution to the IndiSeas Working Group, endorsed by IOC-UNESCO (www. iocunesco. org) and the European Network of Excellence Euroceans (www. eur-oceans. eu).
Acknowledgements. We are extremely grateful to  Hervé Demarcq  ( IRD, France ) for making available to us the chlorophyll and sea surface temperature data he extracted and worked up for the 9 ecosystems examined in our study. Advice from  Steve Mackinson  ( CEFAS, UK ) regarding the North Sea model was greatly appreciated. L. J. S. was supported through the  South African Research Chair Initiative , funded through the  South African Department of Science and Technology (DST)  and administered by the  South African National Research Foundation (NRF). M. C. was supported by the  European Commission  through the  Marie Curie CIG  grant to BIOWEB and the  Spanish Research Program Ramon y Cajal. Y. J. S. and M. T. were supported by the  French  project  EMIBIOS  (FRB, contract no. APP-SCEN-2010-II).
Field sampling Spatial data -the water samples for Chl and pCO 2 were taken between 09:00 LT and 12:00 LT on 1 March 2012 (end of rainy season, high water lever) and 20 September 2012 (end of dry season, low water level). Samples were taken at the surface (0. 3 m) at 42 stations in Funil Reservoir (28 located along the main body of the reservoir, Fig. 1 ) in the same day to limit the effect of diurnal variation on the results. We measured the Chl using a compact version of PHYTO-PAM (Heinz Walz GmbH, PHYTO-ED, Effelrich, Germany). Functionally the PHYTO-ED displays the same features as the standard but has distinct advantages for fieldwork. In the PHYTO-PAM Phytoplankton Analyzer µsec measuring light pulses are generated by an array of lightemitting diodes (LED) featuring 4 different colors: blue (470 nm), green (520 nm), light red (645 nm) and dark red (665 nm).
Back Close Full Chl fluorescence excited at the 4 different wavelengths is obtained. Following proper calibration, this feature allows to differentiate between the contributions of the main types of phytoplankton (green, blue, brown algae) with different pigment systems. In addition, Chl content of the various types can be estimated. The pCO 2 data were determine using water-air equilibration method. In a marbletype equilibrator  (Abril et al. , 2014 (Abril et al. , , 2006)) , water pumped directly from the lake flows from the top to the bottom (0. 8 liters per min), while a constant volume of air (0. 4 liters per min) flows from the bottom to the top. The large gas exchange surface area promoted by the contact with the marbles accelerates the pCO 2 water-air equilibrium. The air pump conduct the air from the top of the equilibrator through a drying tube containing a desiccant (Drierite), then to an infrared gas analyzer (IRGA, LI-840, LICOR, Lincoln, Nebraska, USA), and then back to the bottom of the equilibrator (closed air circuit,  Abril et al.
, 1996)   monitoring of hydrological systems  (Alcantara et al. , 2013; Stevenson et al. , 1993). The SIMA consists of an independent system formed by an anchored buoy containing data storage systems, sensors (air temperature, wind direction and intensity, pressure, incoming and reflected radiation and a thermistor chain), a solar panel, a battery and a transmission antenna. A sonde (YSI model 6600, Yellow Spring, Ohio, USA) was attached to the SIMA buoy to collect hourly surface data on temperature, conductivity, pH, and oxygen. This sonde was calibrated every 15 days according to the YSI Environmental Operations Manual (http://www. ysi. com/ysi/support). We calculated the pCO 2 in the surface water over one year near the dam from measured pH and alkalinity. The calculations include dependence on temperature for dissociation constants of carbonic acid  (Millero et al.
, 2002)  and solubility of CO 2. We used pH and temperature collected by SIMA between 25 October 2011 and 25 October 2012 and monthly data of alkalinity determined by the titration method (APHA, 2005) at station S28 (Fig. 1 ). Samples for total phosphorous (TP) and nitrogen (TN) were taken monthly. For TP, the samples was oxidized by persulfate and then analyzed as soluble reactive phosphorus. TN was determined as the sum of organic fraction measured by Kjedahl method and the dissolved inorganic nutrients. Laboratory analysis for TP and NP was performed according to standard spectrophotometric techniques  (Wetzel and Likens, 2010).
Back Close Full our Chl data collected every 1000 m, proximally, showed a clear transition zone with peaks of Chl. Furthermore, data analysis from only four stations showed that high spatial heterogeneity occurs in periods of high retention time (high water level). To the contrary, our data reveal high spatial heterogeneity in low retention time, corresponding to periods with high influence of the river in the surface water. Different conclusions found by  Soares et al. (2012)  may be explained by the variation of transition zone location. This zone is limited to a small area and its location varies depending on retention time and inflow (Fig. 2c  and d ). Therefore, low number of sample stations in Funil reservoir showed to limit the capacity to represent whole reservoir and to discuss about spatial heterogeneity.
Acknowledgements. This work was supported by the project " Carbon Budgets of Hydroelectric Reservoirs  of  Furnas Centrais Elétricas S. A. ". Thanks  to the  Center for Water Research (CWR)  and its director,  Jörg Imberger , for making ELCOM available for this study. We also thank the  São Paulo State Science Foundation  for financial support ( FAPESP  process no. 2010/06869-0 ). GA is a visiting special researcher from the  Brazilian CNPq program Ciência Sem Fronteiras  (process # 401726/2012-6 ).
[ , 2011]]. The recent development of Next-Generation Sequencing technologies gives rise to new applications along with new difficulties: (a) the increased size of profiles (up to 10 8 data-points when micro-arrays signals were closer to 10 5 ), and (b) the discrete nature of the output (number of reads starting at each position of the genome). Yet applying segmentation methods to DNA-Seq data and its greater resolution should lead to the analysis of copy-number variation with a much improved precision than CGH arrays. Moreover, in the case of poly-(A) RNA-Seq data on lower organisms, since coding regions of the genome are well separated from non-coding regions with lower activity, segmentation methods should allow the identification of transcribed genes as well as address the issue of new transcript discovery.
Our objective is therefore to develop a segmentation method to tackle both (a) and (b) with some specific requirements: the amount of reads falling in a segment should be representative of the biological information associated (relative copy-number of the region, relative level of expression of the gene) and comparison to neighboring regions should be sufficient to label the segment (for instance normal or deleted region of the chromosome in DNA-Seq data, exon or non-coding region in RNA-Seq), so that no comparison profile should be needed. This also suppresses the need for normalization, and thus we wish to analyze the raw count-profile. So far, most methods addressing the analysis of these datasets require some normalization process to allow the use of algorithms relying on Gaussian-distributed data or previously developed for micro-arrays  Chiang et al.