dataesr/scientific-paragraphs-categorization huggingface dataset:
First Screening of Articles The search resulted in 1228 records for the presence of BMAA and/or isomers in the aquatic environment and 1043 records for the aquatic food sources (Figure  1 ). A first check for duplicates was done both in EndNote and manually; all duplicates were removed. The first screening of papers was carried out on titles and abstracts according to several inclusion criteria. The study had to: (i) be written in English; (ii) be an original paper and not a review or a book chapter; and (iii) report a concentration of BMAA, DAB, AEG or BAMA in aquatic environments or aquatic species consumed by humans with the data acquired in situ and not in the laboratory (Figure  1 ). When the abstract of papers did not explicitly describe the work, the full text was read for the first screening step.
0% for oysters, 83. 2% for mussels, 78. 4% in crab meat, and 83. 0% in crayfish  [75]. As a water percentage in fish tissues was not available for every species, we estimated a mean value (i. e. , 74. 8%) based on available data [75] concerning various species (witches Glyptocephalus cynoglossus mean of 82. 1%, plaice Pleuronectes platessa L. mean of 80. 7%, herring Clupea harengus mean of 65. 3%, and Baltic herring Clupea harengus membras mean of 71. 2%). In the absence of information on the water content of the shark cartilage, values remained in mg kg -1 DW.
Goal and Scope The aim of this paper is to identify the environmental hotspots of the conversion of the seaweed Ulva spp. into lactic acid (LA) in the current experimental setup. For this purpose, the Life Cycle Assessment methodology was used with primary data collected in the SEABIOPLAS project. SEABIOPLAS was a European Commission funded research project that aimed to prove the feasibility of producing lactic acid from Ulva spp. cultivated in an Integrated Multitrophic Aquaculture (IMTA) systems  [18, 19]. The standard four-step Life Cycle Assessment (LCA) approach  [20]  was followed in this study. The functional unit was defined as "1 kg of purified lactic acid (LA) and its coproducts, at the bioprocessing laboratory", abbreviated to "1 kg of LA". The lactic acid produced is an intermediate product, which can be applied to the production of several functional materials.
All raw materials and energy that were needed from outside the experimental production chain were included in the study. PLA film was not chosen as functional unit for two reasons: (1) polymerization to alkyd resins was piloted for coatings applications in the SEABIOPLAS project, hence no primary data is available on PLA, and; (2) secondary industrial data of lactic acid polymerization as a separate step was not available, only in aggregated cradle-to-gate format  [3, 4]. Effects of human activities at or near the sea on marine biodiversity and on marine biotic resource availability have been described qualitatively  [21] , but a directly applicable quantitative method is yet to be developed.
3  [25]  for the used resources, including the global average impact of maize cultivation. The Portuguese electricity production was modelled separately using information from the International Energy Agency  [26]  and the system models from EcoInvent 3. 3 of individual electricity generation technologies, and connected to the four unit processes. The enzyme production impacts were taken from  [27]. This procedure results in a supply chain model consisting of several linked processing steps linked with partial LCIs for each supplied resource. The SimaPro software was used to translate this model to one overall LCI for the production of 1 kg of LA and its coproducts, for each scenario.
g. for seed banking, seed production areas;  Broadhurst et al. , 2016). Our findings for population genetic differentiation indicate that it is possible to predict species responses to biogeographic barriers based on seed dispersal vector, which can be integrated with other data to delineate seed zones  (Breed et al. , 2013) , or used to optimize sampling of database collections for tracking timber stocks  (Dormontt et al. , 2015). Spatial genetic structure was most affected by successional stage and seed dispersal vector, and this knowledge can be used to inform seed collection strategies on how to avoid closely related individuals and to ensure representative sampling of population-level variation  (Lowe et al. , 2015). Our findings can also help advance species distribution models by allowing the incorporation of these population genetic functional group classifications into existing simulation frameworks  (Fordham et al.
Protein-fold recognition by the twin-arginine translocase CD spectroscopy Mean residue ellipticity ([] MRW ) of protein samples was measured in a 1-mm path length quartz cuvette on a Jasco J-810 spectropolarimeter with a Jasco PFD-425S Peltier to enable temperature control. Spectra were obtained from 15 to 80 °C at 5 °C intervals. Spectra were recorded continuously at a scan speed of 100 nm min Ϫ1 , with 1-nm resolution and a 4-s response with 4 accumulations. Ellipticity ([]) at 222 nm was measured every 1 °C from 15 to 80 °C at 1 °C min Ϫ1 with a 4-s response. Melting temperatures (T m ) and refolding temperatures (T f ) are the temperature at which 50% of the protein is unfolded or folded, respectively, as determined by fitting melting data to a sigmoidal Boltzmann distribution according to the following equation, where T is the temperature, T m is the melting (or folding) temperature, and T 0 is the initial temperature of the experiment.
Proton NMR spectroscopy Spectra were recorded at 298 K on 0. 2-0. 5 mM protein samples in buffer E, with the addition of 10% D 2 O (spectrometer lock), and 1 mM trimethylsilyl propanate (reference standard). 1 H NMR spectra were recorded using a Bruker Avance 800 MHz spectrometer fitted with a 5-mm QXI room temperature probe, equipped with z axis gradients. One-dimensional experiments were acquired as accumulations of 4096 transients over a spectral width of 24. 038 kHz, corresponding to a proton spectral width of 30. 0 ppm. All data were processed using an EM window function and 5-Hz line broadening, without linear prediction in TopSpin (Bruker). Spectra were referenced to trimethylsilyl propanate at 0 ppm prior to overlay and analysis.
Studentships from  The University of Sheffield. 2Supported by a doctoral studentship from the  Engineering and Physical Sciences Research Council (UK). Present address:  School of Biochemistry, University of Bristol , 3 Supported by  Research Grant   BB/M000265/1  from the  Biotechnology and Biological Sciences Research Council (UK). 4 Supported by the  Marie Curie Initial Training Network Grant  ( Horizon 2020 , ProteinFactory) 642863. 5Supported by a  University of Kent Graduate Training Award studentship. 6Supported by  National Institutes of Health Graduate Fellowship   T32 GM008275 , the  Structural Biology & Molecular Biophysics Training Program. 7Supported by  French "DYNAMO" Grant   ANR-11-LABX-0011-01  for sabbatical year 2015-2016 at UPR9080 CNRS, IBPC. 8Supported in part by  Advanced Award 338895  from the  European Research Council.
1. Assessing the quality of fit of a statistical model to data is a necessary step for conducting safe inference. 2. We introduce R2ucare, an R package to perform goodness-of-fit tests for open single-and multi-state capture-recapture models. R2ucare also has various functions to manipulate capture-recapture data. 3. We remind the basics and provide guidelines to navigate towards testing the fit of capturerecapture models. We demonstrate the functionality of R2ucare through its application to real data.
However when the null hypothesis is rejected, this omnibus approach does not inform about an alternative model that could be fitted. Second, specialized tests have been built to address biologically meaningful causes of departure from the null hypothesis. A global test for single-and multi-state CR models is decomposed into several interpretable components based on contingency tables, for example the presence of transients  (Pradel et al. , 1997; Pradel et al. 2003)  or that for trap-dependence  (Pradel, 1993; Pradel et al. 2003). These GOF tests are implemented in the Windows application U-CARE  (Choquet et al. 2009 ). Here, we introduce the R (R Development Core Team 2014) package R2ucare to perform GOF tests for single-and multi-state CR models. R2ucare also includes various functions to help manipulate CR data.
The R2ucare package The R2ucare package contains R functions to perform GOF tests for CR models as well as various functions to manipulate CR data (see Table  1  and the vignette of the package named vignette_R2ucare). It ensures reproducibility which was not possible with the U-CARE  (Choquet et al. 2009 ) Windows standalone application. Besides, it can be used in combination with other R packages for fitting CR data like RMark (Laake 2013) or marked  (Laake et al. 2013)  or to carry out simulations to assess statistical power (e. g. Bromaghin et al. 2013; Fletcher et al. 2012). We first load the R2ucare package: library(R2ucare) Then we read in the wolf data that is provided with the package. To do so, R2ucare contains two functions that accomodate the most frequent CR formats: read_inp deals with the MARK format (Cooch and White 2006) while read_headed deals with the E-SURGE format  (Choquet et al.
2009 ). The wolf dataset has the MARK format, therefore: wolf = system. file("extdata", "wolf. inp", package = "R2ucare") wolf = read_inp(wolf) We then get the matrix and number of CR encounter histories: ch = wolf$encounter_histories n = wolf$sample_size Following the procedure described in Figure  1 , we first assess the overall fit of the CJS model by using the function overall_CJS: Test 2. CT is significant (χ 2 31 = 64. 45, P < 0. 01). We also provide the signed square root (sign_test) of the Pearson chi-square statistic as a directional test of the null hypothesis  (Pradel et al. 2005) , which is negative when there is an excess of individuals encountered at a given occasion among the individuals encountered at the previous occasion. Note that, by default, the GOF functions in R2ucare returns all the contingency tables that compose the test under scrutiny, which might not be of immediate use and rather cumbersome on screen, hence the use of verbose=FALSE in the call to the test2ct function above.
Now we ask whether there is a transient effect: test3sr(ch,n,verbose = FALSE) ## $test3sr ## stat df p_val sign_test ## 65. 414 29. 000 0. 000 5. 037 Test 3. SR is also significant (χ 2 29 = 65. 41, P < 0. 01). We also provide the signed square root (sign_test) of the Pearson chi-square statistic  (Pradel et al. 2005) , which is positive when there is an excess of never seen again among the newly marked. Navigating through the decision tree in Figure  1   covariates; therefore, these covariates should be removed from the dataset before using R2ucare. For groups, we recommend treating the groups separately (see e. g. the example in the help file for overall_CJS).
Goodness-of-fit tests for the Arnason-Schwarz model We now wish to assess the GOF of the Arnason-Schwarz model to a dataset on Canada Geese (Branta canadensis)  (Pradel et al. 2005). Briefly, the data consist of capture histories for 28,849 individuals marked and re-observed at wintering locations in the US between 1984 and 1986. We first read in the geese data that are provided with the package: geese = system. file("extdata", "geese. inp", package = "R2ucare") geese = read_inp(geese) We then get the matrix and number of CR encounter histories: ch = geese$encounter_histories n = geese$sample_size Then we assess the quality of fit of the Arnason-Schwarz model to the geese CR data with the overall_JMV function. Beware that it takes a minute or so to run the test because an iterative optimization procedure is involved to perform Test M.
Availability The current stable version of the package requires R 3. 4. 3 and is distributed under the GPL license. It can be installed from CRAN and loaded into a R session as follows: install. packages("R2ucare",dependencies=TRUE) library("R2ucare") The repository on GitHub https://github. com/oliviergimenez/R2ucare hosts the development version of the package, it can be installed as follows: if(!require(devtools)) install. packages("devtools") library("devtools") install_github("oliviergimenez/R2ucare") We also maintain a forum at https://groups. google. com/forum/#!forum/esurge_ucare to which questions can be asked.
ACKnOwLeDGMenTS The authors are grateful to  Gareth Griffiths  and  Edouard Yeramian  for their comments on the manuscript. The authors apologize to all colleagues whose work could not be cited due to space limitations. Financial support of our lab is provided by the  European Research Council  ( ERC-STG INTRACELLTB Grant  no. 260901 ,  MM4TB  Grant no. 260872 , and CycloNHit no. 608407 ), the  EMBO YIP program , the  Agence Nationale de la Recherche  ( ANR-10-EQPX-04-01 ,  ANR-14-CE14-0024 ,  ANR-14-CE08-0017 , and  ANR-16-CE35-0009 ), the  Projet Transversal de Recherche de l'Institut Pasteur  ( PTR441  and  PTR22-16 ), the Feder [12001407 (D-AL) Equipex Imaginex BioMed], the  Région Nord Pas de Calais  (convention no. 12000080 ). AM is supported by a fellowship of the  Fondation pour la Recherche Médicale  ( SPF20170938709 ).
IASI Ozone retrievals Ozone retrievals are performed in the 1025 -1075 cm -1 spectral range using the optimal estimation method (OEM)  (Rodgers, 2000)  and tabulated absorption cross-sections at various pressures and temperatures to speed up the radiative transfer calculation. The ozone climatology by  McPeters et al. (2007)  is used as a priori information consisting in one single O 3 a priori profile and variance-covariance matrix. The Eumetsat Level-2 data (pressure, water vapor, temperature and clouds) are used as input in FORLI. It is worth mentioning that the Eumetsat dataset is not homogenous since it has been processed using different versions of the IASI  Level-2 Product Processing Facility between 2008 (v4. 2 ) and 2016 (v6. 2), as summarized in  Van Damme et al. (2017). The error budget of the retrieved O 3 profile shows that the dominant errors originate from the limited vertical sensitivity, from the measurement noise and from uncertainties in the fitted (water vapor column) or fixed (e.
g. surface emissivity, temperature profile) parameters  (Hurtmans et al. , 2012). In order to avoid cloud contaminated scenes, retrievals are only performed for clear or almost-clear scenes with a fractional cloud cover below 13%, identified using the cloud information from the Eumetsat operational processing  (August et al. , 2012). In addition, no retrieval is performed for pixels characterized by an error related to the Level-1C IASI data, by no Level-2 Eumetsat data associated with Level-1C data or by missing temperature, water vapor, surface pressure or cloud value in Level-2 Eumetsat data. The IASI O 3 dataset used in this paper covers the period January 2008 -July 2017. The O 3 product is a vertical profile given as partial columns in molecules cm -2 in 40 layers between the surface and 40 km, with an extra layer from 40 km to the top of the atmosphere.
It also includes other relevant information such as quality flags, a priori profile, total error profile and the averaging kernel matrix, on the same vertical grid. The following quality flags were applied to filter the dataset for further validation analysis. Specifically the data were excluded when: (i) the spectral fit residual root mean square error (RMS) is higher than 3. 5×10 -8 W cm -2 sr cm -1 , reflecting a too large difference between observed and simulated radiances; (ii) the spectral fit residual biased is lower than -0. 75 x 10 -9 W/(cm 2 sr cm -1 ) or higher than 1. 25 x 10 -9 W/(cm 2 sr cm -1 ); (iii) the partial O 3 column is negative; (iv) there were abnormal averaging kernel values; (v) the spectral fit diverged; and (vi) the total error covariance matrix is ill conditioned; (vi) the O 3 profiles have an unrealistic C-shape, with a ratio of the surface -6 km column to the total column higher or equal to 0.
Comparison with ground-based spectrophotometer data Daily TOC measurements from Dobson and Brewer UV spectrophotometers available for the period 2008 -2017 were downloaded from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC, http://woudc. org). The GB stations considered in this paper (see Table  A1  in  Boynard et al. (2016)  for a complete list of the stations) have been extensively used in a series of validation papers of satellite TOC measurements (e. g. Weber et al. , 2005; Balis et al. , 2007a Balis et al. , , 2007b;; Koukouli et al. , 2012 Koukouli et al. , , 2015;; Boynard et al. , 2016). For the IASI-A and IASI-B TOC validation, only direct sun observations are used as ground-based reference data as they are the most reliable for both the Dobson and the Brewer spectrophotometers, the latter offering an accuracy of about 1 % at moderate solar zenith angles (e.
Comparison methodology In this section, we assess the IASI-A and IASI-B O 3 products in terms of four partial columns. IASI vertical profiles are assessed in another paper  (Keppens et al. , 2017). The validation of IASI-A and IASI-B O 3 partial columns is performed using ozonesonde data from the WOUDC and NOAA-ESRL (http://www. esrl. noaa. gov/gmd/dv/ftpdata. html) archives. The sondes provide measurements of O 3 up to 30-35 km with a vertical resolution of ~150 m. Only sonde measurements based on electrochemical concentration cells (ECCs), which measure the oxidation of a potassium iodine (KI) solution by O 3  (Komhyr et al. , 1995) , are used in this study. Their accuracy is generally good (±5 %)  (Deshler et al. , 2008; Smit et al. , 2007)  and their uncertainties are lower than other types of ozonesondes (e.
Figure15presents the monthly relative differences between IASI-A and ozonesonde for the TROPO, UTLS, LMS and MS O 3 columns for the period 2008 -2017 in the NH where there are sufficient collocated data to assess the temporal variation of the differences (more than 40 IASI-sonde pairs per month). For each ozonesonde measurement, a daily relative difference is calculated. All the relative differences are then monthly averaged. To avoid spurious effects due to a seasonal cycle in the differences, only time series of eight years or longer are used, which gathers a total of 20 ozonesonde stations in the NH. A main feature that arises from this figure is the pronounced seasonality in the differences between IASI-A and sonde O 3 for.
Acknowledgments IASI is a joint mission of Eumetsat and the  Centre National d'Etudes Spatiales (CNES, France). The IASI Level-1C data are distributed in near real time by Eumetsat through the EumetCast system distribution. The authors acknowledge the Aeris data infrastructure (https://www. aeris-data. fr/)for providing access to the IASI Level-1C data and Level-2 temperature data used in this study. This work was undertaken in the framework of the Eumetsat AC  SAF  project (http://acsaf. org/), the  European Space Agency O 3 Climate Change Initiative  ( O3-CCI , www. esa-ozone-cci. org)and the  Copernicus Climate Change  ( C3S ). The French scientists are grateful to  CNES  and  Centre National de la Recherche Scientifique (CNRS)  for financial support. The research in Belgium is also funded by the  Belgian State Federal Office for Scientific, Technical and Cultural Affairs  and the  European Space Agency (ESA   Prodex IASI Flow  project).
The ozonesonde data used in this study were provided by the  World Ozone and Ultraviolet Data Centre (WOUDC)  and the  Global Monitoring Division (GMD) of NOAA's Earth System Research Laboratory  and are publicly available (see http://www. woudc. organd http://www. esrl. noaa. gov/gmd). The authors thank all those responsible for the WOUDC and GMD measurements and archives for making the ozonesonde data available. The ground-based total ozone column data used in this publication were obtained as part of  WMO's Global Atmosphere Watch (GAW)  and are publicly available via the WOUDC. We would like to acknowledge and warmly thank all the investigators that provide data to these repositories on a timely basis, as well as the handlers of these databases for their upkeep and quality guaranteed efforts.
Data availability The IASI O 3 data processed with FORLI-O 3 v20151001 can be downloaded from the Aeris portal (http://iasi. aerisdata. fr/O3/; Aeris, 2017). The GOME-2 O 3 data are available on the AC SAF website (http://acsaf. org; AC SAF, 2017). The ozonesonde data can be downloaded from the WOUDC database (https://doi. org/10. 14287/10000008; WMO/GAW Ozone Monitoring Community, 2017a) and from the NOAA-ESRL database (http://www. esrl. noaa. gov/gmd/dv/ftpdata. html; NOAA, 2017). The Brewer and Dobson soundings can be downloaded from the WOUDC database (https://doi. org/10. 14287/10000004; WMO/GAW Ozone Monitoring Community, 2017b). The SAOZ data are available at http://saoz. obs. uvsq. fr (SAOZ, 2017).
Regional climate models (RCMs) are used in a wide range of climate applications as they can provide high resolution (up to 10 to 20 km or less) and multi-decadal simulations of the climate system describing climate feedback mechanisms acting at the regional scale. However due to different forcing data and physics parametrisations regional climate models might produce different results. This study aims to achieve a state-of-the-art knowledge of bias-corrected surface solar radiation projections coming from 11 EURO-CORDEX regional climate models. First a comparison against 63 GEBA observations is elaborated indicating a general overestimation of surface solar radiation (SSR) in the RCMs by 6. 12 W/m 2 (4. 4%). Next changes in surface radiation between the period of 2031-2060  and 1971-2000  are presented on annual and seasonal time scale.
On the other hand, the evaluation of the climate system is controlled not only by natural processes, but also by anthropogenic activities  [8]  difficult to foresee (e. g. greenhouse gas emissions, aerosol loadings). So the uncertainties in climate predictions could originate, on the one hand, from the lack of theoretical knowledge, but, on the other hand, also from the unforeseeable behaviour of the society. Hereafter the present study emphasizes the need of multi-model evaluation of SSR changes before elaborating any impact studies. The paper is organized as follows. Section 2 describes the regional climate models and observational data used in the study, Section 3 presents the results regarding validation against surface observations, Section 4 includes future changes of surface solar radiation in different regional climate models, and Section 5 includes the conclusions and final remarks regarding review of surface solar radiation changes in EURO-CORDEX regional climate models.
Conclusions and remarks The study gives a general review of long-term projections SSR assessing 11 biascorrected EURO-CORDEX simulations. The point-wise comparison with GEBA station data reveals an overall overestimation of SSR in regional climate models by 6. 12 W/m 2 (4. 4 %). The whole distribution of simulated data also presents a shift towards high values. In terms of trends in the historical period model simulations cannot reproduce the global dimming and global brightening periods mainly because no time variant aerosol data are included in the simulations  [7]. Future projections of SSR indicate a slight decrease for the European domain, however regional differences should be considered. The projections are robust (>80% agreement among the models) in the western part of the Mediterranean region indicating increase in SSR, while the northern part of the continent is characterised by less SSR till the middle of the century.