dataesr/scientific-paragraphs-categorization huggingface dataset:
Through a detailed analysis of the FAO database, we have constructed a generalized representation of the nitrogen transfers characterizing the current agro-food system (GRAFS) of 12 macro-regions of the world in terms of functional relationships between crop farming, livestock breeding and human nutrition. Based on this model, and maintaining the current cropland areas and the performance of cropping and livestock systems in each region, we have assessed the possibilities of meeting the protein requirements of the estimated world population in 2050, according to various combinations of three critical drivers namely human diet (total amount of protein consumed and share of animal protein in this total), regional livestock production and crop fertilization intensity, in each region. The results show that feeding the projected 2050 world population would generally imply higher levels of interregional trade and of environmental nitrogen contamination than the current levels, but that the scenarios with less recourse to inter-regional trade generally produce less N losses to the environment.
Finally, at the level of human consumption, the diet patterns plays a significant and crucial role in shaping the agro-food system and its associated environmental N losses, as recently showed by  Westhoek et al (2014). Through a detailed analysis of the FAO database, we have proposed a generalized representation of the nitrogen transfers characterizing the current agrofood system (GRAFS) of 12 macro-regions of the world, defined on the basis of their pattern of international trade exchanges and level of self-sufficiency with regard to their local needs for proteins for feeding humans and livestock (Lassaletta et al 2014a, Billen et al 2014). The analysis first highlights the inequality between the different regions in terms of human diet (total protein intake between 3. 3 and 6. 5 kgN capita -1 yr -1 with 15-58% animal proteins), as well as considerable differences in the efficiency of vegetal to animal protein conversion by livestock systems (from 2.
Acknowledgments This work was partly carried out in the scope of the  EMoSEM  project ( ANR-12-SEAS-0005-01 ) financed by the  French National Research Agency. We wish to thank  Professor G de Marsily  for the lively discussions which sparked our interest in the topic. The idea of the present study emerged during a  Territorial Biogeochemistry course at the University P & M Curie in Paris , where students were asked to imagine a scenario for feeding the world in 2050, starting from the GRAFS analysis of the current situation, and maintaining unchanged the current area and yield/fertilization relationship of cropland, as well as the main functional characteristics of livestock breeding in each of the 12 world regions. From completely vegan to entirely globalized, the scenarios designed by each student differed greatly, reflecting the personality and ideological orientation of their author.
Tethers extensions were vertically shifted for clarity. Events were observed at forces > 5 pN. (C) Single-molecule translocation event by Rho in the presence of ATP (raw data at 34 Hz). Forward and backward velocities for each event were obtained by fitting the linear regions (red and green lines represent linear fits) in the extension versus time curve. Forward (backward) velocities correspond to shortenings (recovery) of the RNA extension. Processivity was measured as the height difference between the baseline before the event (black dashed line) and the minimum extension in a event (cyan solid line). Pause length was measured by determining the time spent in a paused state after RNA shortening and before tether extension was fully recovered (cyan horizontal line). In this example, the forward translocation velocity was 90 ± 10 nt/s, the backward rate was 2100 ± 10 nt/s and the pause duration of 0.
Activity of Rho on a single bead (bead 4, blue) in a field of view with several beads (beads 1-3, cyan, red and green) exhibiting no activity. Raw data are shown(34 Hz). Tether extensions represent relative, not absolute measurements of the tether length. Tethers extensions were vertically shifted for clarity. Events were observed at forces > 5 pN. (C) Single-molecule translocation event by Rho in the presence of ATP (raw data at 34 Hz). Forward and backward velocities for each event were obtained by fitting the linear regions (red and green lines represent linear fits) in the extension versus time curve. Forward (backward) velocities correspond to shortenings (recovery) of the RNA extension. Processivity was measured as the height difference between the baseline before the event (black dashed line) and the minimum extension in a event (cyan solid line).
There is therefore a need to assess the robustness of proxybased PNA reconstructions: if the TT2010 reconstruction is found to accurately capture the past PNA behavior, the reconstructed prolonged strong positive PNA phase during the early 19th century would represent an important feature for Dots mark grid points where the correlation is not significant at 95 % confidence accounting for autocorrelation. The green boxes mark the areas used for the calculation of the PNA index. In panels (b-i), the numbers reported in the respective titles are the spatial correlations between observed and simulated patterns calculated for the domain north of 20 • N (to this end, NCAR data were regridded to the model grid). addressing in coupled climate simulations. Its robust reproduction by climate simulations would be strong evidence for its externally driven nature, while the opposite would suggest two possibilities: either an episodic excitation consistent with internal variability or limited realism of climate models due to common deficiencies.
All data are normalized based on the full period before the pseudo-proxy experiments are conducted. Pseudo-reconstructions are performed as follows: first, the pool of candidate predictors including temperature and precipitation data is determined by defining three regions over North America. Then, an ensemble of predictor sets is built by iteratively (up to 1000 times) and randomly sampling data from one grid point from each region. Finally, an ensemble of PNA pseudo-reconstructions is obtained by using the built sets in a multi-linear regression. Thus, the robustness of PNA pseudo-reconstructions with a TT2010-like design is tested using predictor sets that mimic the quality of the TT2010 reconstruction. Accordingly, we consider only www. clim-past. net/11/939/2015/ Clim. Past, 11, 939-958, 2015 Table  1.
5a ). CCSM4 produces  the strongest and most robust correlations, which overlap with values from reanalyses, whereas FGOALS-gl produces the weakest and most time-varying correlations. The simulated winter PNA-NAO correlations are generally weak and negative during the last millennium, in agreement with the non-significant and strongly varying statistics from reanalysis data (Fig. 5b ). Some simulations feature multidecadal periods when the negative correlation becomes statistically significant, suggestive of a temporarily strong atmospheric connection between the North Pacific and North Atlantic sectors. This is especially the case for CCSM4 (compare also superposing patterns in Figs. 1c  and S2c ). The negative PNA-NAO correlations represent periods when the atmospheric bridge linking Pacific and Atlantic climate variability is active (for a dynamical description see, e.
The Supplement related to this article is available online at doi:10. 5194/cp-11-939-2015-supplement. Acknowledgements. This study stems from discussion during the PAGES2k/PMIP Workshop on integrated analyses of reconstructions and multi-model simulations for the past two millennia (http://www. palma-ucm. es/members/workshop/Home. html) in November 2013, funded by PAGES, FECYT-MINECO and IGEO. Comments from two anonymous reviewers helped improve the clarity of the manuscript. Thomas Kleinen provided useful comments on an earlier version of the draft. D. Zanchettin was supported by the BMBF (MiKlip research program, FKZ:01LP1158A). F. Lehner and C. C. Raible are supported by the Swiss National Science Foundation. This work contributes to the MPI-M Integrated Project Millennium and CliSAP. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output.
Canine population 115 female pet dogs were recruited between 2012 and 2013 in the Veterinary Hospital Center of Oniris (Nantes, France). Serum (1 ml) and subcutaneous adipose tissue (1 gram) sampling were realized during usual surgery procedures. Pet dogs have been treated humanely, and their participation during usual surgery care did not occur until informed consent of owner was obtained. Inclusion criteria were bitches aged over 4 years; exclusion criteria were neoplastic diseases other than breast, mammary dysplasia or hyperplasia (because of their unknown benign or malign outcome), and chemotherapy previous sampling. An epidemiological questionnaire was associated to each canine individual and these data were available in 90% of cases. All these 115 dogs were used to evaluate POPs internal level of exposure in the canine population.
, 1999; Ishii et al. , 2004; Lee et al. , 2006). The A T and C T fields can then be used to calculate pCO 2 fields and thus predict the CO 2 fluxes across the air-sea interface  (McNeil et al. , 2007). Previous empirical approaches to constrain A T in the Mediterranean Sea have only covered selected cruises  (Schneider et al. , 2007; Touratier and Goyet, 2009)  or local areas such as the Dyfamed time-series station or the Strait of Gibraltar  (Copin-Montégut, 1993; Santana-Casiano et al. , 2002). As for C T , empirical models have only been applied to data below the mixed layer depth (MLD) following the equation of  Goyet and Davis (1997)  at the Dyfamed time series station  (Touratier and Goyet, 2009)  or using the composite dataset from Meteor 51/2 and Dyfamed  (Touratier and Goyet, 2011).
The analysis was applied for polynomials of order 1 to 3, and the optimal equation was chosen based on the lowest Root Mean Square Error (RMSE) and the highest coefficient of determination (r 2 ). To ensure the same spatial and temporal distribution of A T and C T polynomial fits we only selected stations where A T and C T were simultaneously measured (Table  1 ; Fig. 1 ). To validate the general use of the proposed parameterizations we tested the algorithms with measurements which are not included in the fits (Testing dataset). Hence spectively. For the C T the training dataset is formed from 381 data points and the validation dataset is the same as the testing subset of the 10th fold (45 data points).
Back Close Full anti-estuarine thermohaline circulation could explain the contribution of temperature to the A T variability  (Touratier and Goyet, 2011). The residuals of training dataset used to generate the second order polynomial fit for A T are presented in Fig. 2a. Most of the A T residuals (340 over 375) were within a range of ±15 µmol kg -1 (1σ). However 35 residuals were high up to ±30 µmol kg -1 (1σ). Applying the A T algorithm to the testing dataset (Fig. 2b ), yields a mean residual of 0. 91 ± 10. 30 µmol kg -1 (1σ), and only 6 data points have residuals higher than ±15 µmol kg -1 (1σ). The comparison of the RMSE as reported by other studies with that of Eq. (  1 ) does not indicate if the parameterization developed here has advanced or not on previous attempts in the Mediterranean Sea.
In that order, we independently applied each of the previous equations on the same training dataset used to develop Eq. (  1 ) and then computed the RMSE and r 2 for every one (Table  3 ). The results show that Eq. (  1 ) has a lower RMSE and a higher r 2 than all of the parameterizations presented in Table  3. For instance, the global relationship of  Lee et al. (2006)  applied to the dataset of this study yields a RMSE as high as ±40. 50 µmol kg -1. The RMSE of other studies developed strictly in the Mediterranean Sea varied from ±13. 81 to ±26. 11 µmol kg -1 using the equations of  Touratier and Goyet (2011)  and  Schneider et al. (2007)  respectively. By applying directly the previous parameterizations to our training dataset, the calculated RMSE are significantly higher than the ones reported in their respective studies.
, 1999; Koffi et al. , 2010). However we found that a third order polynome improved the RMSE and r 2 of the equation compared to the first order fit (Table  4 , Eq. 2). Hence we will retain the large dataset used to develop Eq. (  2 ), where temperature and salinity contribute to 90 % of the C T variability encountered in the Mediterranean Sea surface waters. The remaining 10 % could be attributed to the biological and air-sea exchange contributions to the C T variability. The C T parameterization developed in this study (Table  4 ; Eq. 2) showed a higher uncertainty than that of A T regarding both RMSE and r waters the C T are directly affected by air-sea exchange, and their concentrations will increase in response to the oceanic uptake of anthropogenic CO 2. Previous models accounted for the anthropogenic biases in the C T measurements by calculating the C T rate of increase  (Bates, 2007; Lee et al.
, 2000; Sasse et al. , 2013; Takahashi et al. , 2014). However in a study,  Lee et al. (2000)  also did not correct the C T concentrations for regions above 30 • latitude such as the Mediterranean Sea. In the following we will assess the importance of accounting or not for anthropogenic biases in the C T measurements. In that order we dowloaded the monthly atmospheric pCO 2 concentrations measured from 1999 to 2013 at the Lampedusa Island Station (Italy) from the World Data Center for Green House Gases (http://ds. data. jma. go. jp/gmd/wdcgg/). Following the method described by  Sasse et al. (2013) , we corrected the C T measurements to the nominal year of 2005 and applied the same 10-fold cross validation analysis using data with and without anthropogenic C T corrections. We found that the RMSE of the C T model trained using measurements with anthropogenic corrections is 13.
9 µmol kg -1 , which is not significantly different from the model trained using measurements without anthropogenic corrections (Eq. (  2 ); RMSE = 14. 3 µmol kg -1 ). The yearly increase of C T concentrations is difficult to assess due to the wide spatial distribution of the training dataset used to generate Eq. (  2 ). Hence we will refer to the monthly C T concentrations measured between 1998 and 2013 at the Dyfamed timeseries station. We found that the rate of increase in C T concentrations at the Dyfamed site was 0. 99 µmol kg -1 yr -1 (Fig. 3 ), which is consistent with the anthropogenic C T correction rate used in the previous studies of  Lee et al. (2000) ,  Bates (2007)  and  Sasse et al. (2013). The rate of increase in C T concentrations of 0. 99 µmol kg -1 yr -1 as well as the RMSE difference of ±0.
In regions above 30 • latitude such as the Mediterranean Sea, the corrections of C T are small considering that the outcropping of deep isopycnal surfaces dilutes the anthropogenic CO 2 throughout the water column  (Lee et al. , 2000). Also the dynamic overturning circulation in the Mediterranean Sea plays an effective role in absorbing the anthropogenic CO 2 and transports it from the surface to the interior of the basins  (Hassoun et al. , 2015a; Lee et al. , 2011). The residuals of the dataset used to generate the third order polynomial fit for C T are presented in Fig. 5a  ±50 µmol kg -1 (1σ). Applying the C T algorithm to the testing dataset (Fig. 5b ), yields a mean residual of 1. 48 ± 19. 80 µmol kg -1 (1σ) which is close to the uncertainties of our C T relationship. The high residuals observed in this study are consistent with the results of the optimal multiple linear regression performed by  Lovato and Vichi (2015) , where the largest discrepancies between observations and reconstructed data were detected at the surface layer with RMSE higher than ±20 µmol kg -1.
Summary The A T and C T algorithms are derived from a compilation of 490 and 426 quality controlled surface measurements respectively, collected between 1999 and 2013 in the Mediterranean Sea. A second order polynomial relating A T to both S and T yielded a lower RMSE (±10. 4 µmol kg -1 ) and a higher r 2 (0. 96) than a linear fit deriving A T from S alone. This confirmed the important contribution of temperature to the A T variability. Hence, temperature should be included in future algorithms to help better constrain the surface A T variations. The proposed second order polynomial had a lower RMSE than other studies when we applied their respective algorithms to the same training dataset. In this study we propose an improved and global relationship to estimate the A T spatial and temporal variations in the Mediterranean Sea surface waters.
However in the Eastern marginal seas especially the northern Adriatic and northern Aegean there is a need to develop a more specific equation that minimizes the errors in these areas. Hence, it is important to enrich the existing dataset by an extensive sampling program such as the Med-SHIP initiative (CIESM, 2012) in order to improve the modeling of the carbonate system over the whole Mediterranean Sea. Acknowledgements. The authors would like to thank all parties that have contributed to the data provision in particular: -The Sesame IT4 and Moose-GE cruise data were provided through SeaDataNet -Pan-European infrastructure for ocean and marine data management (http://www. seadatanet. org). -.
-1 and that ofSchneider et al. (2007)  for the Meteor 51/2 cruise is ±4. 2 µmol kg -1. This shows that previous models were constrained by their spatial coverage, time span and used datasets. In fact the previous equations were calculated in local areas such as the Alboran Sea (Copin-Montégut, 1993), the Strait of Gibraltar (Santana-Casiano et al. , 2002) or the Dyfamed Site (Copin-Montégut and Bégovic, 2002; Touratier and Goyet, 2009). On a large scale, equations were applied using limited datasets such as the Meteor 51/2 cruise in October-November 2001 (Schneider Discussion Paper | Discussion Paper | Discussion Paper | et al. , 2007), the Transmed cruise in May-June 2007.
Statistical Analysis Statistical analysis was carried out using the SPSS package 18. 0. Any normal distribution of data was assessed by the Kolmogorov-Smirnov test. As no significant difference was detected in this series between males and females, subjects of both sexes were analyzed together. To analyze differences between obese and normal-weight subjects, and differences between subgroups of obese subjects we used Student's T test for unpaired data or Mann-Whitney test, and Kruskall-Wallis ANOVA followed by Bonferroni's correction as appropriate. Nonparametric Spearman correlation test was used to measure associations between variables and DEHP metabolites. A multiple linear regression analysis was performed to establish which were the major determinants of MEHP urinary concentration. Data are expressed as mean±SEM, and as median and 25 th -75 th percentiles as appropriate.
Discussion The data of this study showed changes in DEHP metabolites related with obesity, onset of puberty, and presence of insulin resistance. Changes in urine metabolites were related also with waist circumference and waist to height ratio, thus to fat distribution. The main limitation of this study stands in the small number of subjects analysed, however, large population studies have addressed issues related to exposure but have not taken into account aspects specifically related with metabolism and very few studies have considered restricted age-groups  [45]. We selected subjects all born and living in a small geographical area within the Emilia-Romagna Region in the North of Italy, an industrialized area, so that significant regional exposure differences should have been excluded. One of the major exposure routes for DEHP, one of the most diffuse phthalates, is represented, however, by food ingestion  [46].
This study was supported by funds of the Dept. of  Paediatrics, University of Parma, Italy , by  Bracco Foundation , Milan, Italy, by  Fondazione Ente Cassa di Risparmio di Firenze (Italy) , the  Regione Toscana (Italy) :  POR CReO FESR   2007-2013 ASSE 1-Linea di intervento 1. 1. C Bando Regionale 2009 , the  ANR(France)  for the Chaire d'Excellence to  AMP  Project PepKit  ANR-09-CEXC-013-01  and the  Ecole Doctorale Sciences et Ingénierie de l'Université de Conclusion  phthalates is actually the key point. Further studies that span from conception to elderly subjects besides further understanding of DEHP metabolism are warranted to clarify these aspects. Cergy-Pontoise  for the PhD fellowship at CT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Although this suggests there should be selection for early breeding, the spread of laying dates can span over five months within a breeding season  [17, 22, 23]. These variations in parent's ability to prepare for breeding support the hypothesis that there are some carry-over effects from the inter-breeding season to breeding. Here, we used a detailed, longitudinal dataset of body mass of known individuals recorded every time the individual moved in and out of the colony over a 7 year period to further develop our understanding of the importance of winter condition on subsequent breeding. Specifically, using this dataset, we were able to separate 'annual effects', from within-year 'individual effects'. We hypothesized that individuals in better condition during the inter-breeding season will be in a better position to breed earlier and perhaps attain greater reserves (or capital) in the following months leading up to breeding (i.
On one given day (i) of each month, a 1004 g object was weighed n times (n ranged 3-10) on the weighing platform and an average deviance (x) of the monitoring system weight from 1004 g was calculated (i. e. x (i) = Σ(1004 g-monitoring system weight (i) )/n (i) ). All records of body mass between the day of the calibration and the previous calibration were adjusted by adding the mean deviance. Outliers were detected and excluded from the data analysis, which included records with a body mass outside the known range for little penguins at Phillip Island between 550 and 2130 g  [24]  and beyond the maximum change in body mass for a foraging trip between 1 and 14 days long (maximum change in weight = 420 g, mean 101. 1 g ± 8; A. Chiaradia 2003 Chiaradia -2004, unpublished data), unpublished data).
Data analysis 3. 3. 1. Changes in body mass along the inter-breeding season Three factor variables and their two-and three-way interactions were used to predict changes in body mass: inter-breeding season periods (moult-recovery, winter, pre-breeding), year (2002-2008 as a factor) and sex (male or female). Body mass was first standardized to account for the sexual body size dimorphism. Standardized body mass was calculated as an individual's body mass relative to the global mean and standard deviation of body mass for the relevant sex. Repeated measures of each individual were accounted for using linear-mixed models (LMMs), where the random effect was individual identity and the error distribution was Gaussian with an identity link function. Where there was a significant interaction, between-group effects were assessed (i.