dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection for scenarios Soil data were collected from previous studies implemented in the area. Transmissivity values were taken from  Meylan (2012)  and soil cohesion, bulk density and friction angle from  Nespoulous (2011). The number of roots crossing a given surface area of soil was derived from  Prieto et al. (2015). Root tensile strength was estimated using a generic equation (e. g. Genet et al. , 2005; Kuriakose, 2010; Mao et al. , 2012 ): = T α d * rn n β (1) Where T rn is the tensile strength for the n root and d n the diameter of the n root; α and β are coefficients specific for every species. These factors were also used by  Mao et al. (2012 Mao et al. ( , 2014)). According to  Mao et al. (2012) , the α and β coefficients do not differ significantly between tree and shrub species.
Modeling approach To investigate the suitability of LAPSUS_LS to model landslide catchment processes and the influence of vegetation, we decided to use input data from field studies. Using real data will help our understanding of the adjustment and calibration that the model needs after a ground truth studies or after selecting input data from the literature. For example, the soil cohesion factor (c s ) in this study was determined for a soil with its water content close to that found in the field during the sampling period, which was 7-20% below the field capacity. However, the LAPSUS_LS model was developed to work with cohesion levels determined at soil saturation  (Claessens, 2005). Pellet et al. (2013)  and  Ghosh (2012)  state that clay soils in particular experience a drop in c s at soil saturation.
(3) in discussion and (S4) (S5) in Supplementary materials). The sensitivity analysis was carried out by varying the collected input data (Table  1 ) by ± 10% intervals. The values were increased until reaching a +100% of the original value and decreased until reaching the 0% values, to plot the sensitivity of the model to the different input data. The model was than modified to include biomass surcharge in the calculation of critical rainfall values (modification of Eqs. (S4)-(S6), see Supplementary materials). Several runs were performed for different c s and c r values including the influence of biomass surcharge. The results of the model with and without biomass surcharge were compared to understand its influence on the LAPSUS_LS simulations.
Discussion LAPSUS_LS was sensitive to differences in both vegetation and soil parameters, but the differences observed in the two land uses depended on the initial input data. The soil cohesion values  (Nespoulous, 2011)  were relatively high in comparison to existing literature  (Claessens et al. , 2005 (Claessens et al. , , 2006a (Claessens et al. , ,b, 2007;; De Sy et al. , 2013; Keijsers et al. , 2011) , because direct shear tests were performed at a wetness index 7 -20% lower than field capacity. These data resulted in an overestimation of c s since LAPSUS_LS was developed to work with cohesion levels measured at soil saturation  (Claessens et al. , 2007). This is particularly true for clay soils that can experience the higher drop in cohesion due to water saturation  (Pellet et al. , 2013; Ghosh 2012).
Conclusion The LAPSUS_LS model was sensitive to the vegetation parameters tested, in particular to the additional cohesion from roots. As vegetation characteristics are highly heterogeneous in a catchment, coupling LAPSUS_LS with a model that can predict vegetation heterogeneity (especially root heterogeneity) could greatly improve the performance of the model. We also showed that the effect of surcharge from vegetation is negligible; therefore it is not necessary to include this parameter in LAPSUS_LS. The reliability of LAPSUS_LS is dependent on how carefully the input data are selected. Therefore, to model the influence of vegetation it is fundamental to select reliable soil input data. The heterogeneity of a catchment, the biases of the procedures used to produce data and the simplifications needed, make selecting reliable input data a challenging task.
Acknowledgements We are grateful to the  Agence Nationale de la Recherche in France  for funding this work (Ecosfix  ANR-10-STRA-003-001 ) and the  H2020-MSCA-ITN-2015  project ( TERRE675762 ). The authors wish to acknowledge the support of the  European Commission  via the  Marie Skłodowska-Curie Innovative Training Networks (ITN-ETN)  project  TERRE  'Training Engineers and Researchers to Rethink geotechnical Engineering for a low carbon future' ( H2020-MSCA-ITN-2015-675762 ).
Microarray Data Processing The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  (Edgar et al. , 2002)  and are accessible through GEO Series accession number GSE67708 (www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE67708). Raw intensity values were processed using the R package oligo  (Carvalho and Irizarry, 2010). Individual probes raw expression values were computed from. xys files and the in house pd info builder package pd. vitus. exp. vitnames designed to fit the 12Xv1 annotation nomenclature. Normalization was performed with Robust Multi-Array Average (RMA;  Irizarry et al. , 2003). Resulting RMA expression values were log 2transformed. Distributions of expression values processed via RMA of all arrays were very similar with no apparent outlying arrays.
Microarray Data Analysis Each condition (clone × stage/organ) was performed in three biological replicates. Differential expression analyses of the comparisons presented in Table  1  were performed with the ebayes  (Smyth, 2004 ) method from the package limma in R. The cutoff of differentially expressed genes was set to a p < 0. 05 after Benjamini-Hochberg correction with at least a 2-fold ratio difference of expression. Principal component analysis (PCA) was performed in R using the pca package with the ppca method. Hierarchical clustering was performed using MultiExperiment Viewer  (Saeed et al. , 2003)  based on Pearson's correlation and using the average linkage option and optimal gene ordering. The stringent set was obtained by clustering genes with a distance threshold <0. 05. The tolerant set was obtained by clustering genes with a distance threshold <1.
FUNDING This work was financially supported by the projects  AGL2014-59171R  (co-funded by  FEDER)  and  AGL2010-15694  and the  Ramon y Cajal  grant  RYC-2011-07791 , all from the  Spanish MINECO. JT was the recipient of a predoctoral fellowship from  MINECO  (Grant:  BES-2011-047041 ).
14, 15 On the contrary, published data on some pulmonary drug delivery systems have shown acute toxicity in mice. In fact, surface hydrophobicity of nanoparticles from 50 to 150 nm was characterized by hydrophobic interaction chromatography (HIC). The acute lung inflammation was quantified after pulmonary administration to mice. The correlation between nanomaterial HIC index and acute lung inflammation was determined for LNCs, polyvinyl acetate and polystyrene nanoparticles. This study showed that LNCs have a low HIC index and induce little inflammation unlike polyvinyl acetate and polystyrene nanoparticles characterized by high HIC index and increase acute alveolar toxicity. 16 In another study, the relationships between nanoparticle hydrophobicity, inflammatory outcomes and the metabolic fingerprint in bronchoalveolar fluid from mice exposed to various nanoparticles were studied following a single-dose intratracheal administration.
For this purpose, the acquired images were analyzed and processed on a Xeleris 2 workstation (GE Healthcare). Regions of interest (ROIs) were drawn manually on various organs (lungs, liver, stomach, spleen, trachea) on the planar images. Time-activity curves for the LNCs-99m Tc-SSS-and the 99m TcO 4 -controlinjected rats were obtained by plotting the ROIs on the images for the various acquisition times. The activity in the ROI was expressed as a percentage of the injected dose. To compare the lung clearance (Cl) kinetics of the LNCs-99m Tc-SSS and the 99m TcO 4 -controls, several pharmacokinetic parameters such as lungs' half-life (T 1/2 ), mean residence time (MRT) and Cl were calculated using a non-compartmental model for the analysis of the pharmacokinetics data (Kinetica 5. 1 software; Thermo Fisher Scientific).
Acknowledgments This work was performed with the financial support of the  Institut Régional en Santé Respiratoire dans les Pays de la Loire (rue du Landreau, Beaucouzé, France) , the  Comité départemental du Maine-et-Loire et de Charente ,  Cancéropôle Grand Ouest  and the  Bretagne, Centre, Pays de la Loire and Poitou-Charentes Régions. This study was performed with the technical assistance of  Mr P Legras  and  Mr J Roux  of the  Angers University Hospital animal facility  and  Mrs A Godefroy.
Data analysis All data were analyzed in MATLAB (RRID:SCR:001622). For the behavioral experiments, lick rate was acquired at 1 KHz and smoothed using convolution with a Gaussian filter of 50 ms standard deviation. Mean anticipatory licking was calculated for each trial as the mean lick rate in the period of 500-2800 ms after odor onset, after subtracting the mean lick rate over a baseline period of À500 to 500 ms around odor onset. To evaluate the aversiveness of the air puff delivered to the mice in the photometry experiment, we used a CCD camera (Point Grey) to record the right eye of six mice during several sessions at 60 Hz. To quantify blinking in video data, we manually selected the eye area in each session and calculated the mean pixel value for that area; then, for each frame, we subtracted this value from the previous frame to obtain a measure of movement.
The start and end of blinking created a sudden increase and decrease, respectively, in the difference between the mean pixel value of consecutive video frames. In the time course analysis of the licking behavior in the hM4D experiment, trials of sessions around reversal were concatenated and smoothing over three trials was performed along the trials. For each reversed odor and each mouse, the last 50 trials before reversal were fit by a constant function of the form (A+B); the first 200 trials after the reversal were fit by an exponential function of the form (A+B*exp(-t/t)) using fminsearch in MATLAB. The conditions for this fitting to be done were: the last 100 trials before reversal had to be statistically different from trials 100-200 after the reversal (t-test), the change in licking pattern had to follow the correct trend of the reversal (increase in licking for positive reversals and decrease in licking for negative ones), and the time constant obtained had to be larger than 1.
Mouse-odor pairs that did not fulfill this condition were excluded (that is, odor 4 of mice M#4 and M#5). Time constants were grouped according to the type of reversal and genotype with drug treatment, and compared using one-way ANOVA. Then, for each SERT-Cre mouse, the time constant of the reversal with the vehicle was subtracted from the reversal with CNO. The same was done for WT mice, but subtracting the time constant of reversal two from that of reversal 1 (since CNO was delivered in both). ttests were used to determine whether these differences had means significantly different from zero. Fluorescence data were downsampled to 1 kHz and smoothed using convolution with a Gaussian filter of 100 ms standard deviation. For each trial, the relative change in fluorescence, DF/F 0 = (F-F 0 )/ F 0 , was calculated by taking F 0 to be the mean fluorescence during a 1 s period before the odor presentation for both the red and the green channels ([DF/F 0 ] GREEN and [DF/F 0 ] RED ).
For each session and each mouse, the distribution of green and red values of DF/F 0 was fitted by the sum of two Gaussians along the red channel, and the crossing point between these two Gaussians was used as a boundary (excluding the first and last 1000 ms of each trial because of filtering artifacts). All values of [DF/F 0 ] RED below this boundary were used, together with the corresponding [DF/F 0 ] GREEN , to fit a linear regression line. Then, for each trial we corrected the green DF/F 0 values using the parameters (a -slope; b -offset) obtained with the regression model of that mouse in that session: [DF/F 0 ] GREEN_- corr = [DF/F 0 ] GREEN -a*[DF/F 0 ] RED -b. Behavioral data were organized as a function of US type and divided into CS and US responses. [DF/F 0 ] GREEN_corr US responses were normalized by subtracting the mean [DF/F 0 ] GREEN_corr over the 1 s interval before US onset.
Acknowledgements We thank  R M Costa  and  J J Paton Labs  for TH-Cre mice,  Susana Dias  and  Sergio Casimiro  for histology and immunohistochemistry assistance, and  Dario Sarra  for running behavioral experiments for a few days. We also thank  C Poo ,  B V Atallah ,  M Murakami ,  G Agarwal  and  J J Paton  for comments on a previous version of the manuscript, and all members of the  Systems Neuroscience Lab  and the  Champalimaud Research  for useful discussions and feedback during the development of this project. This work was supported by the  Fundaca ˜o para a Ciencia e Tecnologia  (fellowship  SFRH/BD/43072/ 2008  to SM),  Human Frontier Science Program  (fellowship  LT000881/2011L  to EL),  European Research Council  (Advanced Investigator Grants  250334  and  671251  to ZFM) and  Champalimaud Foundation (ZFM).
Data and methodology Di¤erent methodological approaches for an analysis of the economic e¤ects of an EMU-UI system are possible. While previous research has mainly used aggregate macro level data, we rely on representative household micro data for the EA18 using EUROMOD, a static tax-bene…t calculator for the European Union countries. EUROMOD is mainly based on cross-sectional micro data from the EU Statistics on Income and Living Conditions (EU-SILC) released by Eurostat (Eurostat 2012) which we combine with micro data from the EU Labor Force Survey (EU-LFS). 9 The key advantage of our approach in the present context is that we exploit both detailed income distribution information contained in EUROMOD as well as information on changing labor market patterns over time contained in the LFS.
Earnings growth is imputed from the AMECO-database in order to account for changes in the tax base of the EMU-UI and national UI systems. These imputations ensure that our reweighted micro data are consistent with aggregate statistics in each year of our simulation period (see Technical Appendix A. 2 for further information). The analysis at the subgroup level allows us to examine individual heterogeneity within each member  9 Sutherland and Figari (2013)  provide more detailed information on EUROMOD, the underlying input data and validation. The EU-LFS, conducted by the national statistical institutes across Europe and processed by Eurostat, is a representative household survey covering the years from 1983 onwards. It is the most important source for labor market statistics in the EU. Cf.
Automatic …scal stabilization Automatic …scal stabilization is associated with the ability of taxes and transfers to automatically stabilize disposable income and consequently consumption in the event of macroeconomic shocks. This relies on a simple mechanism: in the presence of a 11 Detailed policy rules of national UI systems are collected from country chapters of the OECD series "Bene…ts and Wages" (http://www. oecd. org/social/bene…tsand-wages. htm) and from the EU's MISSOC-Comparative Tables Database (http://ec. europa. eu/social/main. jsp?langId=en&catId=815). Actual coverage rates are imputed from the EU-LFS. 12 See Craig et al. (2016) for an analysis of state government behavior with regard to bu¤er-stock savings in the UI program in the US. Bargain et al. (2013) account for labor supply behavior after the introduction of a European tax and transfer system and …nd small labor supply responses.
Neumann acknowledges support from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement no. 269831. All results and their interpretation presented in this paper are the authors' responsibility. This project is related to the research agenda of the  ADEMU  project, " A Dynamic Economic and Monetary Union. "ADEMU is funded by the  European Union 's  Horizon 2020 Program  under grant agreement  N° 649396  (ADEMU). _________________________ The ADEMU Working Paper Series is being supported by the  European Commission Horizon 2020 European Union funding for Research & Innovation , grant agreement No  649396.
Although there has been no definitive assessment of the collection 135 efficiency for biomass burning, previous studies in biomass-burning-dominant areas have used this figure (see e. g. Brito et al. , 2014). The SP2 was used to measure the concentration of refractory black carbon (rBC) in the emitted particles. rBC is carbonaceous, insoluble and vaporises only at temperatures near 4000 K  (Petzold et al. , 2013; Schwarz et al. , 2008). The operation and process of data interpretation have been described by  Liu et al. , (2010)  and  McMeeking et al. , 140 (2010). Particles pass through a 1064 nm laser beam inside the instrument and the consequent incandescent and scattered light is detected on a single-particle basis. The intensity of the laser is sufficient to vaporise absorbing material, which re-emits radiation in the visible spectrum on incandescence.
185 Eight flammability experiments were undertaken, with different heat fluxes and flow rates. These, and the notation used to refer to them, are presented in table  1. Due to the emphasis placed on reproducibility during these tests, some aspects differ from real-world biomass 190 burning and these must be taken into account when comparing these results with emissions reported elsewhere. The heating of samples throughout the experiments allowed greater reproducibility by reducing the effects of the external conditions, and as a result these data are more representative of burning of a larger accumulation of material. Emissions here are sampled in a duct rather than from an ambient environment and as such, there is no effect of mixing with ambient air.
Emission factors In atmospheric models, the emissions from wildfires, cookstoves and other sources of BBA are often represented by average emission factors (EFs), which relate the production of a given pollutant to the mass of fuel that has been burned (e. g. Liousse et al. 2010). The average EFs employed by these models are measured in the field or in 490 laboratories for specific fuels and, occasionally, burning conditions. They are compiled into inventories, such as those presented by  Andreae & Merlet (2001)  and  Akagi et al. (2011). However, very large variation in EFs can be found even within individual datasets; uncertainties are attributed to fuel type and characteristics, combustion phases, fire intensity and the naturally chaotic behaviour of fires  (Reid et al. 2005). Results presented here provide some insight into some of the mechanisms that result in this high variability.
Areas affected by storms are also more likely to vary across time, as areas affected by floods need to be close to rivers or to the sea. Storms have been reported in every European country except Finland and Serbia, whereas eight European countries have not reported any flood during the sample period used in this study. In addition, flood defence mechanisms may reduce the likelihood of re-occurring floods or their impact on GDP, therefore potentially compromising the exogeneity of the instrument. Data on extreme weather events are publically available in the Emergency Events Database EM-DAT (Guha-Sapir, Below, and Hoyois 2014). EM-DAT contains indicators that measure storms in different ways. 4 Storm occurrences, rather than any of the alternative measures, have the advantage of being more likely to be a precise, objective, and exogenous measure.
However, this comes at the expense of treating all storms equally, regardless of their impact. Nevertheless, the EM-DAT database only comprises those storms that had a significant impact, i. e. 10 or more people reported killed, 100 or more people reported affected, declaration of a state of emergency, or call for international assistance. We are also mindful that reporting might be heterogeneous across countries, although we believe that this potential problem has limited relevance in our sample, since most European countries are industrialised economies with established monitoring authorities and reporting requirements. Under-reporting is not likely to be an issue as European countries have an incentive to correctly report disasters, since they become eligible to emergency aids to cope with immediate damages as well as long-term investments in prevention from the European Union's Solidarity Fund and the Civil Protection Mechanism.
As the markets for biomass and minerals are mainly local, we investigate whether storms have an impact on those prices, as a statistical association would cast doubts on our instrument. We estimate the following models: Dp i;t ¼ k Dstorms i;tÀh þ ' t þ u i þ y i;t (1) where ' t are time effects, u i are country fixed effects, storms i;tÀh indicates the number of storm occurrences. These are taken from the EM-DAT database unless explicitly stated. The letter h indicates the time lag with h 2 0; 1; 2 ð Þ. p i;t indicates the price of biomass, material inputs, and mineral prices as dependent variables in the three different models we estimateas shown in Table  1. To proxy biomass prices, we chose real industrial crop prices in Europe between 2000 and 2014 from Eurostat. As shown in Columns 1-3, we find no significant relationship between storms and crop prices.
In the case of material input prices, Columns 4-6, we consider the correlation between the number of storm occurrences and the material input prices for constructing residential building from Eurostat in Europe between 2000 and 2014. As shown in Table  1 , we find no statistically significant association between storms and material input prices across time. In the case of mineral prices, Columns 7-9, we use data from the Nordic Statistic Database on the mineral costs of dwelling construction and the number of storms from the Extreme Wind Storms Catalogue for Finland, Sweden and Denmark from 2000 to 2013. The Nordic countries are the only group of countries that collect mineral price data using a standardised approach, both in terms of data collection and variable definition. As shown in Table  1 , we find that the relationship between storms and mineral prices in the current period is significant at the 10% level and negative.
Data description Materials, i. e. usable substances obtained or derived from natural resources, consist of biomass, metals, minerals, and fossil fuels  (EC 2017; OECD 2007). Material flow analysis (MFA) is an established method to calculate material use indicators  (OECD 2008). MFA has been increasingly standardised over time and adopted by several statistical offices in industrialised countries, including all European countries  (Hinterberger, Giljum, and Hammer 2003). We use data on DMC in tonnes per capita for 32 European countries between 2000 and 2014, extracted from Eurostat. DMC is the only measure of material use with publically available data which is directly comparable across our sample (EC 2015a). 8 For economic activity, real GDP in Euro (in PPP) per capita is extracted from Eurostat.
An alternative approach is to generate random variables, i. e. variables with random values, but with the same mean and standard deviation as our instrument and use these variables with the same statistical properties as our instrument to estimate our model by 'placebo' 2SLS regressions. If the results were similar to those displayed in Table  3 , our instrument could be questioned  (Bound, Jaeger, and Baker 1995). This approach is chosen over using alternative instruments as a robustness check, because other measures of storms, including their severity measured by the number of people affected, are only imprecisely and sparsely reported in the EM-DAT database. We also believe that the number of storms is more likely to be an exogenous indicator than measures of storms' severity which are likely to be affected by the level of economic development.