dataesr/scientific-paragraphs-categorization huggingface dataset:
The liquid thickness in the custom-designed XRR cell (cf. Figure  1 ) was 1 cm, permitting ~20% transmission at 14 keV X-ray energy, which is sufficient for XRR measurements  [41]. Specular XRR scans were collected with the incident angle θ i varying from 0. 06-3. 0º and the reflection angle θ r = θ i , corresponding to a momentum of transfer Q = 4πsin(2θ/2)/λ range of 0. 015-0. 74 Å -1 , where 2θ = θ r + θ i. The specular reflection was detected at each angle using an avalanche photodiode detector (APD), as shown in Figure  1. XRR data fitting was performed by using Motofit in Igor Pro (WaveMetrics, Inc. , Lake Oswego, OR, USA), using the Abeles matrix method, which generates the same result as Parratt's recursive method  [42]. A genetic algorithm optimisation is adopted in this software to minimize the χ 2 value, which shows the goodness of fitting, χ 2 = 1 L -P y 0,n -y n 2 L n=1 , Equation 1 where L is the number of collected data points; P the number of variables which are varied during fitting, e.
AFM imaging at the graphene-water interface AFM topological images (Figure  3a ) have also been collected to reveal the morphology of the graphene-water interface and were processed using the NanoScope Analysis software. We have previously studied the The topological images of the graphene sample in air, which indicated that the surface was comprised of the SiO 2 substrate, graphene, and the defects (highlighted in white rectangles in Figure  3a ) that consisted of PMMA and multilayer graphene flakes  [38]. However, it was a lot more challenging to obtain good quality, reproducible images under water, which is probably ascribable to a more complicated structure with inhomogeneous coverage. In addition, the presence of soft air bubbles could make it difficult for the AFM scanning tip to engage with surface.
Acknowledgement We acknowledge funding from the  Engineering and Physical Science Research Council  ( EPSRC EP/H034862/1 ,  EP/K035746/1  for PeakForce atomic force microscopy that was carried out by  Dr R. Harniman  in the  Chemical Imaging Facility, University of Bristol ). Funding from the  Royal Society , the  European Cooperation in Science and Technology (CMST COST ) Action  CM1101 , the  Consejo Nacional de Ciencia y Tecnologia (CONACyT)   Postdoctoral Fellowship 291231 , and the  Marie Curie Initial Training Network (MC-ITN) "Soft, Small, and Smart: Design, Assembly, and Dynamics of Novel Nanoparticles for Novel Industrial Applications (NanoS3 )" ( FP7  Grant No. 290251 ) was also acknowledged. We also thank  P&G  for financial support via the  Bristol Final Year Industrial Project Scheme.
Coffman, Bernstein and Wee (2016)  [19]  provide a review on factors affecting PEV adoption. They show that the public charging infrastructure is an important factor associated with PEV uptake. In addition, they identify that actual purchases are much lower than consumers' stated preferences derived from studies primarily relying on surveys about hypothetical situations. Rezvani, Jansson and Bodin  [20]  review consumer PEV adoption studies presenting a comprehensive overview of the drivers for and barriers against consumer adoption of plug-in PEVs. Kühl et al. (2019)   [21]  analyze German Twitter data and literature on customer needs concerning e-mobility. Price-related needs and needs concerning car characteristics are overrepresented in literature. On the other hand, charging-related needs are particularly overrepresented in the Twitter data set.
For the case of MeHg, one of the explanatory variables was IHg concentration (precursor of MeHg). The data is given in the supplementary material (Aveir-o_Sediments_DistanceR. xlsx) as well as the R scripts text files. Both Y and X i were separately normalized, if necessary, using graphical visualization and Box-Cox transformations  [28]  to give variables Y T and X T,i. Some of the explanatory variables (Al, Mn, TC, d S , d M , d R ) were not altered, with only the following transformations finally being selected: = = = = = = = = = = IHg IHg MeHg MeHg Ca Ca OM OM FF FF OC OC DR DR d d DR DR d d ln ( ) ln ( ) ln ( ) ln ( ) ln ( ) ln ( / ) ( ) / T T T T T T M T M S M R T R R S , , 0. 5 (1) The relationship between the explanatory variables is expressed by L 1 , L 2 or L 3 , the index representing the highest interaction order, described in the starting model: … … = + + … … = + … … = = = = L X X X X a X a X X a X X X L X X X X a X a X X L X X X X a X ( , , , , , ) ( , , , , , ) ( , , , , , ) T T T i T q i q i T i i j i j T i T j i j k i j k T i T j T k T T T i T q i q i T i i j i j T i T j T T T i T q i q i T i 3 ,1 ,2 , , 1 , , , , , , , , , 2 ,1 ,2 , , 1 , , , , 1 ,1 ,2 , , 1 , (2) The regression coefficients a i represent the simple terms for variable X i.
Second, the applicability of the obtained model was checked after removing values of Y higher than 95th percentile (P 0. 95 ) of all data and the regression coefficients for the minimal adequate model were determined again with the remaining dataset (short data, n = 30). The obtained regression equation, using the short data, were extrapolated for data removed (extrapolated data, n = 2). The minimal adequate model was considered further only if robust, meaning the same significant terms were obtained for all data and for short data. All models were compared using graphical representation of model values Y M against experimental ones Y E and by root mean square deviation (RMSD) criteria (for all data, short data and extrapolated data) representing how different the model values are when compared with n experimental data of Y: = RMSD Y Y n ( ) E i M i , , 2 (16).
T. Stoichev  et al. Journal of Hazardous Materials 367 (2019) 715-724 Acknowledgements T. Stoichev is grateful to  FCT for fellowship   SFRH/BPD/88675/ 2012 , co-financed by  Programa Operacional Potencial Humano (POPH) / Fundo Social Europeu (FSE). The financial support of  European SUDOE Interreg IVB Programme  through the  Orque-Sudoe  project and of  FONDECYT  project ( 1150855 ) is acknowledged. J. P. Coelho was supported by the  Integrated Program of SR&TD  ' Smart Valorization of Endogenous Marine Biological Resources Under a Changing Climate'  (reference Centro- 01-0145-FEDER-000018 ), co-funded by  Centro   2020 program , Portugal  2020 ,  European Union , through the  European Regional Development Fund.
Bioinformatic Treatment, Functional Annotation, and Sequence Analysis Raw data generated by 454 pyrosequencing (Genbank/EMBL/DBJ accession number: PRJNA517634) were preprocessed using SnoWhite software  (Dlugosch et al. , 2013)  to remove nonsense sequences including (i) adapters used for reverse transcription and 454 sequencing, (ii) primers, (iii) very short (<15 bp) sequences, and (iv) low quality sequences. In a first step, the preprocessed sequences were assembled with optimal parameters (-minlen 15 -it 0 -ig 0 -icc 0 -icl 5000 -mi 95 -ml 10 -ss 1 -sl 16 -a 15 -rip) using runAssembly (Newbler software, Roche version 2. 6) supplied by Geneouest (France). In a second step, the same preprocessed sequences were mapped to the transcriptome resulting from the first assembly using runMapping (Newbler software, Roche version 2.
6) with optimal parameters (-minlen 15 -notrim -ss 1 -sl 16 -sc 1 -ml 80% -mi 95% -a 15 -d 15) to obtain the final transcriptome used for the analysis. To annotate the obtained contigs we compared them with the available non-redundant (nr) NCBI protein database using blastx software with an E-value cut-off of 10 -4. To assess the potential function of proteins encoded by contigs, we used the Gene Ontology (GO) controlled vocabulary and more particularly GOSlim, a subset of GO terms, which provides a higher level of annotation  (Vincent et al. , 2010). To this end, an automated GO-annotation of contigs was performed using the Blast2go software and a stringency cut-off of 10 -6. To identify peptide signals and transmembrane domains the assembled sequences were translated into the correct open reading frames (ORFs) using Prodigal  (Hyatt et al.
, 2010)  and amino acid sequences were analyzed using signalP 4. 0 1   (Nielsen, 2017). SignalP identifies potential signal peptides in amino acid sequences and locates their cleavage sites. In a previous study, we found that most proteins corresponding to highly expressed genes and predicted to contain a peptide signal by SignalP were indeed confirmed by proteomic analysis to be present in the venom of the parasitoid wasp Chelonus inanitus  (Vincent et al. , 2010). In addition, we also used the DeepLoc-1. 0 program that predicts the localization (subcellular or extracellular) of eukaryotic proteins 2. Gall wasp contigs were specifically compared using blastx and tblastx programs to available parasitoid wasp transcriptomes from NCBI public databases and from a homemade database collecting sequences of venom components reported from a selection of parasitoid species.
Gall Wasp Ovary and Venom Gland 454 Sequencing Statistics and de novo Assembly The 454 run provided a total of 292784 and 304506 reads respectively for D. rosae and B. pallida samples. Quality control using SnoWhite software resulted in a total of 287438 cleaned reads for D. rosae and 298180 reads for B. pallida, with average sequence lengths of 336 bp and 369 bp, respectively (Table  1 ). To obtain an optimal assembly, sequences from ovary and venom gland cDNA libraries from the same species were merged together, using parameter settings that allowed us to obtain large contigs and to avoid chimeras (see Materials and Methods). The cleaned reads were assembled into 2061 contigs for D. rosae and 2304 contigs for B. pallida (Table  1 ). Similar high quality of sequencing and assembly was obtained for both species with contig sizes of an average length of 330 and 339 bp (N50 447 and 462 bp) for D.
Initial Annotation and Origins of Expressed Genes Contig annotation was performed by blastx comparison with sequences in non-redundant public databases using an E-value cut-off of < 10 -4 and a match was identified for 54 % of contigs from each wasp species (1117 contigs from D. rosae and 1253  contigs from B. pallida) (Table  1 ). The E-value of most contigs from both species ranged from 10 -15 to 10 -50 and 35% of contigs from D. rosae and 32% of contigs from B. pallida shared very high similarities with sequences in databases (E-value < 10 -50 ) (Supplementary Figures  S1a, c ). For both D. rosae and B. pallida, 97% of matched contigs were identified as insect sequences, primarily hymenopteran (Supplementary Figures  S1b, d ) as expected. Interestingly, a Adapters, primers, very short reads (<15 bp), and low-quality reads were removed using SnoWhite software.
Identification of Potential Virulence Factors To identify potential virulence effectors more likely involved in gall induction we focused on genes coding for potentially secreted proteins, since virulence factors are likely to be found among secretion products of venom glands or ovarian epithelia, based on what we know about parasitoids. We therefore considered here proteins that are expected to be secreted by the classical ER-Golgi secretory pathway and do not contain a transmembrane domain that would suggest they are anchored to the cell surface (see Materials and Methods). Since numerous transcripts lacked the 5 end and hence the region potentially encoding a signal peptide, we also retained proteins showing homology to known secreted proteins present in the databases. A total of 167 B.
pallida contigs and 134 D. rosae contigs encode proteins containing a signal peptide or which are homologous to proteins known to contain a signal peptide. In both species, a higher proportion of genes potentially encoding proteins with predicted signal peptides was expressed in venom glands compared to ovaries (Figure  5 ), reflecting the important secretory function of the glandular epithelium of venom glands. In B. pallida, among contigs differentially expressed in the venom glands at least 29. 5% of the contigs and 12% of contigs with no match in databases encode proteins with predicted signal peptides, which therefore likely correspond to venom proteins. By comparison, only 1 or 2 % of contigs belonging to the other categories coded for proteins with a signal peptide (Figure  5A ), suggesting that ovaries are less actively secretory than venom glands (which could be expected since egg production is their major function).
Similarly, in D. rosae, 21. 4 % of the identified contigs and 13% of nonassigned contigs differentially expressed in the venom glands encode proteins with predicted signal peptides, compared to only 1 or 2 % for the other categories (Figure  5B ). Interestingly, the DeepLoc1. 0 analysis of the deduced sequences from contigs differentially expressed in venom glands from B. pallida and D. rosae confirmed a probable extracellular addressing for 38 of the 46 contigs predicted to encode a peptide signal for B. pallida and for 12 out of 17 contigs for D. rosae (Tables  2, 3 ). It is noteworthy that B. pallida unassigned contigs differentially expressed in venom gland share no similarity with D. rosae contigs, except for one sequence (coding for a protein with a putative DNA binding domain -Pfam 05485-) belonging to the "equivalently expressed" category in D.
Identification of Potential Virulence Factors Differentially Expressed in the Venom Gland Transcriptome of D. rosae In D. rosae, 271 contigs were differentially expressed in venom glands, most of which potentially code for proteins with no similarity to known proteins in databases and only 28 of which could be annotated. Of these, 17 contigs code for proteins with a predicted signal peptide or with high similarity to proteins possessing a signal peptide (Table  3 ). The global pattern of gene expression in the venom gland of D. rosae differs from patterns observed in B. pallida (Figure  7B  and Table  4 ) -in particular, the cellulase genes whose transcripts were detected in the B. pallida venom gland were not detected in D. rosae. However, some venom gland transcripts were differentially expressed in both B.
LC-MS All flavonolignans and taxifoline were quantified using a LC-MS analysis performed on a Water A factorial experiment design and the resulting response surface plots were used to identify the optimal extraction conditions for all flavonolignans using XLSTAT2015 software (Addinsoft, Paris, France). Variables were coded at three levels (-1, 0 and +1;     1 ). Here, twentyseven batches were obtained by using the DOE (design of experiment) function of XLSTAT 2019 which take values of selective variables at different levels (Table  2 ). The experiments were carried out in triplicate. Equations of the models were calculated using XLSTAT 2019 DOE analysis tool. Surface plots showing the response as a function of the simultaneous variation of the independent variables were obtained with 3D option of XLSTAT 2019.
Funding: This research was part of the  MariLine program  focusing on the cosmetic application of the milk thistle flavonolignans, supported by  Cosmetosciences , a  global training  and research program dedicated to the cosmetic industry. Located in the heart of the cosmetic valley, this program led by  University of Orléans  is funded by the  Region Centre-Val de Loire. Acknowledgments: S. D. and L. G. acknowledge research fellowships of  Loire Valley Region. D. T. gratefully acknowledges the support of  French  government via the  French Embassy in Thailand  in the form of  Junior Research Fellowship Program  2018. B. H. A. gratefully acknowledges the support of  Cosmetosciences and Le Studium Institute for Advance Study International Research Fellow  funded by  Region Centre-Val de Loire.
Salts retention The concentrations of NaCl, Na 2 SO 4 and Na 3 PO 4 in the feed solu tions were adjusted to 0. 1 g L -1 of each in mixed salt solutions. To determine the anion concentrations, an ion chromatograph machine (Metrohm 883 Basic IC Plus, USA) equipped with an anion separation column (Metrosep A Supp 5 100/4. 0, Metrohm, USA) and software MaglCnet 3. 1 was used. The sample loop was 20 µL and a conductivity based detector was used. The chemical suppression was performed with 100 mM H 2 SO 4 and a mobile phase of 5 mM Na 2 CO 3 /5 mM NaHCO 3 was applied at a flow rate of 1. 0 ml min -1. Furthermore, single salt solutions containing 0. 1 g L -1 of CaCl 2 were also prepared. The con centration of CaCl 2 was measured with a conductivity meter (Consort C3010, Belgium). Finally, the retention value R was calculated ac cording to Eq.
Acknowledgments The authors want to express their gratitude towards  Benjamin Horemans ,  Peter Van den Mooter ,  Peter Salaets , and  Muhammad Azam Rasool  from the  Faculty of Bioscience Engineering of KU Leuven. They also would like to deeply thank  Mr. Thierry Trotouin  from the  Veolia Company  for his continuous support and help. This project was ac complished under the framework of the  EUDIME program  (doctoral contract No. 2014 122 ), financially supported by the grant  N. IOF KP/ 13/004.
We collected information on body size, body shape, leg size and body toughness following the study of specimens under the stereomicroscope. The data on species locomotion, camouflage, period of activity, mode of ingestion and dispersal ability was recorded during fieldwork, collected from the literature  [57] [58] [59]  or obtained from experts. For morphospecies some traits were collected directly from specimens, but others were assigned considering the available information for close relatives (e. g. congeneric species). A detailed description of the nine functional traits along with their ecological relevance is given in Table  1.
Data analysis Taxonomic and functional alpha diversity. For each plant species, we pooled the data on insects of the sampled individuals per transect and this was considered our unit of analysis. The first set of analyses aimed to test how phytophagous insects' TD and FD differed between the five study plants. TD was assessed by the second-order Jackknife species richness estimator (Jack2) while species evenness was calculated using the Smith and Wilson's index on log-transformed abundance data  [60]. Insect herbivore species richness completeness of each study plant was assessed from the ratio of observed species richness to the values obtained from Jack2 richness estimator using the correction proposed by Lopez et al. [61]. Then, we assessed the differences on species richness completeness of the five study plants using a non-parametric Kruskal-Wallis one-way analysis of variance.
Acknowledgments The authors want to thank all the people involved in the project " Biodiversity of Arthropods  from the  Laurisilva of the Azores" (BALA)  ( PROJ. 17. 01_080203 ) during field and laboratorial work, particularly  Clara Gaspar ,  Fernando Pereira  and  Isabel Amorim. We are also thankful to  Clara Gaspar  for providing information on insect functional traits.
Backward-based Model of the Studied HET The EMR and control are merged to obtain an equivalent backward-based model (Fig. 6 )  [36]. The model organization is conserved but the inputs and outputs are changed. Moreover, the control is assumed perfect and the EMS provides only the control inputs. For the needs of the DPM, this model is implemented in Matlab© code  [31]. The objective of the DPM is then to determine the optimal control inputs.
Comparisons of the studied HETs The DPM is used to perform a fair comparison between the AMT-based and CVT-based HETs. As the DPM is an offline optimization method able to determine the theoretical optimal solution of a non-linear and complex problem, it can be used to obtain a benchmark in terms of fuel consumption. The different optimal solutions for different topologies, configurations and technologies can therefore be compared in a fair way on the same driving cycle. Furthermore, the DPM leads to EMSs which deliver the control inputs for the vehicle controls. If the optimal EMSs are calculated off-line from a backward-model, they are then imposed to the forward-based simulation in Matlab-Simulink © based on the EMR organization. In addition, the DPM imposes the energy balance in the battery, which is necessary to perform fair comparisons of fuel and energy consumption.
Acknowledgements The authors would like to thank  Pascal Charrier  for his valuable help during the XCT and  Laurent Debove  for helping create the oedometer cell and  Olumide Okubadejo  for his help developing the dDIC. Laboratoire 3SR is part of the  LabEx Tec 21  ( Investissements d'Avenir-Grant  Agreement No. ANR-11-LABX-0030 ). The material was kindly provided to the  University of Edinburgh by CWK, Germany.
Despite its importance in sealing, a fundamental understanding of the behavior of elastomeric O-rings in a gas environment is still incomplete. Further experimental research is needed to obtain a precise predictive model which can describe the combined effects of gases and mechanical loading. The effect of CO2 pressure (2, 4 and 6MPa) on the mechanical compression behavior of HNBR and FKM seals at two temperatures (60 and 130°C) is described in the paper. To evaluate the contribution of nanofiller reinforcement, experimental tests were carried out on two kinds of rubber which are reinforced or not with nanofiller (10 phr of expanded graphite). A ranking of materials was performed thanks to experimental database. HNBR seems to be the better candidate in the service conditions applied. Furthermore, the nanofillers which are introduced in the rubber can lead some damages in Rapid Gas Decompression conditions, notably when the zero pressure was applied (desorption).
Material Models Seals with different materials (HNBR, FKM, HNBR-10GE, and FKM-10GE) are tested under uniaxial compression to establish load-displacement and deviation-displacement curves, so that an appropriate component model can be selected in the following finite element (FE) simulations. The tests were carried out around 25-30% compression, which corresponds to average deformations. To this end, a hyperelastic modeling model can be used to describe the behavior of elastomeric seals in our FE simulations. These simulations are performed using the commercial FEA (Finite Elements Analysis) software ABAQUS (User's Manual Version 6. 13, Providence, RI, USA)  [16]. To describe the behavior of the elastomers in this study, the neo-Hookean compressible form of the strain energy density function was chosen.
Acknowledgments The authors gratefully acknowledge the  European Research Council  (project No. 638857 ) and the  Ministry of Education, Youth and Sports of the Czech Republic  (projects LM  2015041 ,  LM2015082 ,  LQ1601  and  CZ. 02. 1. 01/0. 0/0. 0/16013/0001829 ) for financial support of this work, whose part was carried out with the support of  CEITEC Nano Research Infrastructure. This work has been carried out thanks to the support of the A* MIDEX  project (n •  ANR-11-IDEX-0001-02 ) funded by the  Ïnvestissements d'Avenir" French government program , managed by the  French National Research Agency (ANR).
Atomic force microscopy (AFM) experiments were performed using the Nano-Observer device from CSInstrument. The topographic images were obtained at room temperature in tapping mode. Images were processed with the Gwyddion free SPM data analysis software. Transmission Electron Microscopy (TEM) images were recorded in bright field mode with a microscope operating at 120 keV (JEOL JEM 1400). A 20 micron objective aperture was used in to improve the contrast. Active layers films were spun cast on PEDOT: PSS coated glass substrates before being floated off in deionized water, collected on copper TEM grids (Electron Microscopy Sciences) and dried in a desiccator over night before the analysis.
Acknowledgments The authors thank  Dr Sylvie Dabos-Seigon  from  Moltech-Anjou  for the AFM images and  Romain Gallet  ( SCIAM, University of Angers ) for TEM experiments. This work was financially supported by the project SMOSCs, ID:  37_220 ,  Cod MySMIS : 103509  funded by the  Romanian Ministry for European Funds  through the  National Authority for Scientific Research and Innovation (ANCSI)  and co-funded by the  European Regional Development Fund/Competitiveness Operational Programme   2014-2020 (POC) Priority Axis 1/Action 1. 1. 4.