dataesr/scientific-paragraphs-categorization huggingface dataset:
At this stage, the draft toolbox was applied to policy and multi-component activities that were already running or that were soon to be implemented or disseminated in different countries, to test and further enrich the toolbox. Stakeholders involved in these policies and interventions were asked to apply the toolbox and complete structured feedback sheets. The toolbox was tested in the context of five policies and six multicomponent interventions and feedback was provided by the relevant users. Finally, a meeting to reach a consensus on the content, appearance, accessibility, and user-friendliness of the toolbox was organised among the TA3 partners. Overall, the stakeholder evaluation of the usefulness, feasibility, and applicability of the toolbox was positive; feedback provided indicated that the database part of the toolbox provided good examples of policy and multi-component interventions, and that the toolbox was helpful in terms of planning future policy and multi-component interventions and their evaluation.
The series of systematic literature reviews assessed the available evidence on the current nature of dietary, physical activity and sedentary behaviour in Europe, as well as on factors that influence these behaviours over the life course. These revealed a great deal of variation in terms of assessment methods used and reported behavioural outcome variables, such that information on physical activity, sedentary and dietary behaviour across all of Europe is scarce, and such that studies in this area are difficult to compare and findings are difficult to assimilate. The disabling lack of knowledge exposed by the systematic reviews may well stem from the dearth of available data on determinantsat least in Europe. The various secondary data analyses that were undertaken in the context of DEDIPAC highlighted a lack of data that prevents closure of the knowledge gap.
These findings, as well as those from the other DEDIPAC frameworks, support the suggestion, and call for, a shift in focus from individual responsibility, personal commitment, and lifestyle choices, to the influence of social and physical environment on overcoming barriers to healthy behaviour. The efforts made in the context of the secondary data analyses on determinants of physical activity and sedentary behaviour faced considerable barriers across all FAIR domains: data resources, associated metadata, and complimentary files were often not easy to find ('Findable'). Retrieving relevant metadata from these datasets, on the types of variables, age groups under study, study design, measurement instruments used, time frame, etc. , for instance, was a painstaking process (' Accessible'). A consistent data format and taxonomy for knowledge representation was generally lacking ('Interoperable'), and, finally, only a limited number of datasets were eventually found to be 'Reusable' , i.
e. , made available. Extending beyond FAIR, the harmonisation of independent variables and outcome measures under study was often problematic. The DEDIPAC Compendium of Datasets indicates substantial variation in assessment methods and operationalisation of variables across current European studies. This variation not only hampered the practical harmonisation process, but also presented comparability issues, as estimations of physical activity and sedentary behaviour levels are known to differ based on the assessment method used. The secondary data analyses' focus on ethnic-minority groups revealed that within current 'mainstream' research on the determinants of health behaviour in Europe, ethnic minorities are often either not included, or their numbers are too small to enable meaningful analysis.
Conclusions Europe has the right ingredients for the creation of infrastructure to study the causes of the causes of the chronic disease burden. DEDIPAC has strengthened existing infrastructure by aligning countries, research centres, and scientists from various disciplines across Europe on this crucial topic. It provided further insights into the measurement, the wide range of determinants of dietary, physical activity and sedentary behaviour across the life course and their interplay. However, action is now needed to build on this momentum. At present, we need better cross-European harmonisation of measurement and monitoring, FAIR data management and data sharing, common methodology, as well as (longitudinal) data required to gain more insight into behavioural determinants, as well as policy evaluation and benchmarking.
Acknowledgements Not applicable. Amsterdam School of Communication Research (ASCoR), University of Amsterdam, Amsterdam , The Netherlands. 2  Department of Public and Occupational Health, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands. 3 Sydney School of Public Health, University of Sydney, Sydney, NSW, Australia. 4Leibniz-Institute for Prevention Research and Epidemiology -BIPS, Bremen, Germany. 5INRA ,  UR1303  ALISS, F-94205 Ivry-sur-Seine, France. 6Department of  Nutrition, University of Oslo, Oslo, Norway. 7Department of  Movement and Sports Sciences, Ghent University, Ghent, Belgium. 8Department of  Movement, Human and Health Sciences, University of Rome Foro Italico, Rome, Italy. 9 Institute for Applied Health Research, School of Health and Life Science, Glasgow Caledonian University, Scotland, UK.
The most frequently distributed and best-studied cis elements are the AU-rich elements (AREs) that are often arranged in mRNA 3′-UTRs as repeated AUUUA pentameric sequences that eventually overlap  (9). More than 6,000 human ARE-containing mRNAs have been listed in the last upgrade of the ARED 3. 0 database (http://brp. kfshrc. edu. sa/ARED/) (10). Many of them have a short half-life, rendering this regulatory process highly effective to rapidly turn down a cellular function.
Body size was defined as the distance from the top of the head to the base of the tail spine and measured in mm from photos, using the image analysis software ImageJ  (Rasband 1997 ). Age at a given developmental stage (IM-1, IM-2 and IM-3) was recorded as the number of whole days to reach that stage, and the number of instars was also an integer, representing the number of prematuration instars required to reach IM-1. Growth rates in mm per day were derived from the slope coefficients of linear regressions fitted to   et al. 2013) , and because allocation of resources between competing demands of growth and maturation are likely to be influenced by both genetic and environmental cues  (Glazier & Calow 1992; Hart & Bychek 2010) , two separate growth rates were calculated. Early growth considered age and size values up to and including IM-1; and late growth considered age and size values inclusively between IM-1 and IM-3.
To further investigate interactions between fixed effects, ANOVAs and generalised linear models were used as appropriate to investigate effects of these factors at individual trait level. For each ANOVA/GLM model, selection based on Akaike's Information Criterion (AIC) was used to remove non-significant interactions and effects. In cases where interactions with maternal environment effects were maintained, false discovery rate (FDR) corrected post hoc tests were then carried out with the fixed effects clone and/or offspring environment held constant (ANOVA, GLM and post hoc test results are presented in the Supporting Information). ANOVA, MANOVA and GLM analysis were performed, using the STATS package of the R language for statistical computing (R Development Core Team 2014) with the packages MASS  (Venables & Ripley 2002)  and HMISC  (Harrell 2013 ) loaded, and post hoc tests were carried out with the testInteractions function in the PHIA package (De Rosario-Martinez 2015) with the package CAR loaded  (Fox & Weisberg 2011 ).
PCA and PCVs were calculated from all directly measured offspring traits using data scaled to unit variance (estimates of PMRNs are unsuitable for incorporation into multivariate analyses). Following PCA, co-variation among traits was identified from high loadings onto the same principal component. Multivariate clone 9 maternal environment 9 offspring environment means were projected onto a plot of the first two principal components in order to aid interpretation of the statistical results. Maternal environment PCVs were defined as multivariate mean vectors: D Y ¼ D Y ij À D Y ik for treatment group i (maternal environment) in environments j and k (e. g. offspring food environments of 133 and 59 cells per lL for a given clone). The magnitude of the vector is calculated as the Euclidean distance: D E ¼ jjD Y i jj ¼ ðD Y i D Y T i Þ 1=2 , where T represents a matrix transpose.
A comparison of the difference in length (DD) and/or angle (h) of maternal environment PCVs between any two clones for a given offspring environment was used to statistically test for clonal variation in the effect of maternal environment, and a comparison of DD and/or h for the same clone in any two offspring environments was used to statistically test whether maternal effects were context dependent or not. A full description of PCVs can be found in  Collyer & Adams (2007) , and the application of this approach to Daphnia life-history data is described in  Plaistow & Collin (2014). The STATS package of R was used for both analyses (R Development Core Team 2014), and PCA was carried out, using the FACTOMINER package  (Lê, Josse & Husson 2008 ).
Data from all offspring environments was included in PMRN analysis, although offspring environment was not considered as a variable per se, as variation in this factor is necessary to produce the diverse growth trajectories that enable PMRN estimation. As in  Harney et al. (2013) , the importance of different offsets was considered (age, size and none); backwards stepwise term deletion was used to test the importance of interactions between factors (clone and maternal environment) and covariates (age and size); and age and size were modelled as either interval start-, ©2017 The Authors. Functional Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society. , 31, 1996-2007 mid-, or end-points using log-transformed or untransformed values. Likelihood ratio tests (LRTs) were used to compare nested models, and AIC or likelihood comparisons were used to compare non-nested models.
Acknowledgements S. J. P. was supported by a  Natural Environment Research Council  ( NERC) postdoctoral fellowship grant  ( NE/C518214/1 ); and E. H. was supported by an  NERC  doctoral training grant while at Liverpool and a grant from the  Regional Council of Brittany , from the  European Funds (ERDF) , and by the  "Laboratoire d'Excellence" LabexMER  ( ANR-10-LABX-19 ) through a grant from the  French government  under the program " Investissements d'Avenir ". We also thank three anonymous reviewers for their helpful comments on an earlier version of the manuscript, and  Dieter Ebert  for originally providing some of the clones.
MeIQx initial concentrations The data set used for this present work has been previously published  (Langouët et al. , 2001). Briefly, human hepatocytes isolated from human liver biopsies were treated with low concentration of MeIQx (1, 10 and 50 µM) not to induce cytotoxic effects  (Langouët et al. , 2001). The culture media were retrieved at 24 h and the metabolites were purified and quantified by mass spectrometry. Figure  2  shows the percentage of total metabolite quantity for the ten following compounds: MeIQx, MeIQx-N 2 -Gl, MeIQx-N 2 -SO 3 H, N-desmethyl-7-oxo-MeIQx, 7-oxo-MeIQx, IQx-8-COOH, 8-CH 2 -OH-IQx, HON-MeIQx-N 2 -Gl, HONH-MeIQx, and Potential-Genotoxic-Compound. To describe the metabolism of MeIQx with a numerical model based on ordinary differential equations (ODE), we first converted each compound percentage in molar concentration by using a proportional relationship: [compound] t =24 = 1 100 [MeIQx] t =0 * (percentage of total compound concentration) t =24.
Model fitting, parameters estimation, and a posteriori filtering The parameters of all ODEs numerical models were selected such that the model predictions optimally fitted with the data set from the experiment HL-1 (Table  1 ) at t = 24 h. For this, we denoted by t 1 = 0 and t 2 = 24 h the two time-points introduced in Table  1. We also introduced d 1 = 1 µM, d 2 = 10 µM and d 3 = 50 µM the initial concentrations of MeIQx used in experiments. We also denoted by C 1 ,. ,C 8 the compounds that include 7 compounds from Table  1  and HONH-MeIQx that is not detected. In this setting, we denoted by y obs k,l,i the measured concentration of each compound C k at time t l in HL-1 hepatocytes in presence of initial MeIQx concentration d i. Together, we obtained 48 experimental data points. All models describe the variations of the eight variables C 1 ,.
,C 8 according to time. Each model is parameterized by a set of parameters θ , including (i) the 16 fixed parameters of the model and (ii) the parameters allowing to specify the values of the distribution parameters α and β according to the saturation and dose-dependent hypotheses. The prediction concentration of a compound C k at time t l in HL-1 hepatocytes in presence of initial MeIQx concentration d i is denoted by y k (θ ,t l ,d i ). The PottersWheel toolbox for Matlab software  (Maiwald & Timmer, 2008)  was used to sample the family of all possible parameters set θ. The main steps of the method are as follows. First, a random set of parameters is selected. From this set of parameters, a trust-region algorithm is applied iteratively to decrease the following mean-square distance  (Raue et al.
Model-fitting suggests that the dose-dependance hypothesis is more relevant than the saturation hypothesis As detailed in Material and Methods, in order to study the metabolism of MeIQx in humans, we set up a scheme of numerical models using ordinary differential equation (ODE) based on the data from the experiment using human hepatocytes HL-1 reported in  Langouët et al. (2001). Two hypotheses were introduced to model the enzyme competition process according to the initial MeIQx concentration and tested by first searching for the sets of parameters which best explain the data and then testing their relevance with individual data. As shown in Table  2 , the saturation hypothesis (line 1) cannot be explained with the family of parameters identified by the fitting procedure. Indeed, all models reported by PottersWheel toolbox procedure had a p-value lower than 1, which is too low to be considered as relevant.
Model validation based on literature datasets Among the detoxification metabolites, the major one is the C-Hydroxy-MeIQx derivatives  (Langouët et al. , 2001). We first explored the behavior of C-Hydroxy-MeIQx and HONH-MeIQx-N 2 -Gl for short time and low concentrations, corresponding to the usual human exposures. For this purpose, we used different values of parameters corresponding to the 73 valid numerical models. For MeIQx initial concentrations between 0. 05 to 0. 5 µM, the formation of C-Hydroxy-MeIQx reaches a plateau (steady-state) before 7 h of treatment (Fig. 5 ). At this time, for any initial dose less than 0. 5 µM, the predicted quantity of C-Hydroxy-MeIQx varies from 30% to 70% of the initial dose. This is consistent with data from  Gu et al. (2010)  reporting that C-Hydroxy-MeIQx accounted for 35.
Influence of CYP1A2 on balance between bioactivation and detoxification The PotterWheel matlab toolbox was used to perform a 2D sensibility analysis of the influence of each parameter over the system. As depicted in Fig. S1 , this analysis highlighted that the total concentration of CYP1A2 is a key actor of bioactivation products formation. In order to illustrate and understand better the influence of this parameter, the parameter value of the total concentration of CYP1A2 was modified in the 73 models (multiplication by 10, and division by 10). The perturbed models were simulated in order to compute   Simulations were performed in three different conditions: a normal condition where the total CYP1A2 concentration of MeIQx corresponds to the parameter values learned for the 73 models (D, E, F), a lowconcentration condition where the CYP1A2 concentration of each of the 73 models is decreased by a factor 10 with respect to the initial models (A, B, C), and high-concentration condition where the CYP1A2 concentration of each of the 73 models is increased by a factor 10 (G, H, I).
However, our analysis suggests that the predicted ratio between bioactivation and detoxification pathways could be considered as an indicator of MeIQx genotoxicity at low concentrations of MeIQx. More importantly, our model clearly illustrates the importance of introducing regioselectivity phenomena in in-silico modelling approaches. Indeed, our analyzes suggest that this mechanism may occur on CYP1A2 regulation and therefore highly impact the balance between bioactivation and detoxification. Future work will mainly consist of testing the relevance of our modelling approach in a wider context of multi-organ global exposition. Indeed, a main limitation of the model is that is restrained to human hepatocytes since the parameters were learned according to hepatic metabolism data. Although the liver is known to be the major organ of detoxification (justifying the choice of developing a model for this organ) and our predictions are by extrapolation consistent with data coming from oral exposition, it will be highly interesting to compare the MeIQx dynamics in other human organs as soon as the corresponding datasets are available.
Another obvious limitation of the model is that it is patient dependent since the fitting was based on datasets extracted from human patients with no information on patient characteristics such as enzyme polymorphism. In this setting, the available data are not sufficient to take bioavailability and tissue distribution into account and to implement a population model. Nonetheless, we consider that our approach is a first seminal step to pave the way towards the development of complex models  (Zhuang & Lu, 2016; Kuepfer et al. , 2016)  by pointing out the importance of integrating complex models for enzyme regulation in multi-organ PBPK models.
Measurements A variety of measured data were used to evaluate the model experiments. The measured dataset was based on the global GMOS monitoring network for Hg  (Sprovieri et al. , 2016 (Sprovieri et al. , , 2017;; GMOS, 2016) , complemented by data from the European Monitoring and Evaluation Programme (EMEP) regional network for Europe  (Tørseth et al. , 2012; EMEP, 2016)  and by data from the Mercury Deposition Network of the National Atmospheric Deposition Program (NADP/MDN)  (Prestbo and Gay, 2009; NADP/MDN, 2016) , the Atmospheric Mercury Network (AMNet)  (Gay et al. , 2013; AMNet, 2016) , and the Canadian National Atmospheric Chemistry Database (NAtChem)  (Cole et al. , 2013; Steffen et al. , 2015; NAtChem, 2016)  for North America. Available measurements of GEM, GOM, and PBM concentrations in air were compiled, as well as wet deposition flux measurements performed at ground-based sites in 2013.
GEM in air RM in air Hg wet deposition In total, the dataset included 49 sites measuring GEM, 14 sites measuring RM, and 124 sites measuring wet deposition. GEM observations are relatively uniformly distributed over the globe, with somewhat higher density in the Northern Hemisphere. In contrast, RM is observed mostly in northern temperate latitudes, with only a few sites located in the tropics and further southward. Most wet deposition measurements are located in North America and Europe, limiting the possibility of model evaluation in other regions.
All the models used the same Hg anthropogenic emissions dataset (AMAP/UNEP, 2013a, b), with somewhat different speciations of mercury forms in the base case. In the other model experiments, the emissions speciation was harmonized by assuming all emissions to be a form of GEM (Table  2 ). By contrast, total values of natural and secondary emissions differed significantly among the models. Parameterization of these processes is an essential element of the model setup describing Hg cycling between the Earth's surface and the atmosphere, which cannot be easily changed or removed without disturbing the entire model balance. The major chemical mechanisms in the standard model configuration in the base case were also essentially different. The base-case reactions of GLEMOS and ECHMERIT included Hg oxidation by O 3 and OH.
, 2012) , and the reaction rate constants were obtained from  Goodsite et al. (  2012 ),  Donohoue et al. (2006), and Balabanov et al. (2005). The GEM surface fluxes included anthropogenic sources, biomass burning, and geogenic activities as well as bidirectional fluxes in atmosphere-terrestrial and atmosphere-ocean exchanges  (Song et al. , 2015). Biomass burning emissions were estimated using a global CO emissions database and an Hg / CO volume ratio of 1×10 -7. Geogenic activities were spatially distributed based on the locations of Hg mines. For atmosphere-terrestrial exchange, GEOS-Chem treated evasion and dry deposition of GEM separately  (Selin et al. , 2008). Dry deposition was parameterized by a resistance-in-series scheme  (Wesely, 1989). In addition, effective GOM uptake by sea-salt aerosols was also included over the ocean  (Holmes et al.
Emissions data A global inventory of Hg anthropogenic emissions for 2010 (AMAP/UNEP, 2013a, b) was used in the study. The original dataset consisted of gridded emission data with a spatial resolution of 0. 5 • × 0. 5 • for three Hg species (GEM, GOM, and PBM). Total global Hg emissions from anthropogenic sources were estimated at 1875 tonnes per year, with the overall shares of GEM, GOM, and PBM emissions equal to 81, 15, and 4 %, respectively. As mentioned earlier, some models modified the original speciation of anthropogenic emissions (Table  1 ) in the BASE case simulation. No information on temporal variability of emissions was available in the dataset. Geographically, significant Hg emissions were predicted in industrial regions of East and South Asia, central Europe, and the eastern part of North America (Fig.
In contrast, assuming additivity of Hg processes in the atmosphere with respect to contributions from different sources, the effect of natural and secondary emissions (NoNAT) could be estimated by subtracting the NoANT results from the BASE case. Four additional model experiments were intended to evaluate different chemical mechanisms of GEM oxidation in the atmosphere. To avoid the influence of direct anthropogenic emissions on simulated RM concentrations, all emissions were assumed to be a form of GEM. The BrCHEM1 and BrCHEM2 model runs included a single mechanism of GEM oxidation by atomic Br, but using two different datasets of Br concentration in the atmosphere: one simulated by GEOS-Chem  (Parrella et al. , 2012)  and the other by p-TOMCAT  (Yang et al. , 2005 (Yang et al. , , 2010)).
A comparison of the spatial and temporal variations in Br concentration from these two datasets is given in Figs. S7 and S10 in the Supplement. Two other experiments, O3CHEM and OHCHEM, were based on O 3 -and OH-initiated oxidation chemistry. The models used the same O 3 and OH concentration datasets extracted from MOZART model simulations results  (Emmons et al. , 2010). The spatial gradients and seasonal variation of the reactants are shown, respectively, in Figs. S8-S9 and S11-S12 in the Supplement. Note that not all models were used to perform the whole simulation program. The results presented below are based on available simulations for each particular experiment.