dataesr/scientific-paragraphs-categorization huggingface dataset:
Second, the P-value is also used to decide the validity of the null hypotheses. If the P-value shown in Table  4  for a selected factor is smaller than 0. 001, it can be said that the factor has a significant effect on the examined mechanical property. For instance, in this study when the effect of rubber content on the ultimate flexural stress is investigated, after executing two-way ANOVA, degrees of freedom in the numerator (R wt. %) was found "2" as shown in Table  4  whereas degrees of freedom in the denominator was found as 39. By using F-  Moreover, to verify the significance of the AF and recycled EPDM rubber incorporation on the mechanical properties, two-way ANOVA statistical analyses were implemented on the results reported in Table  3  by using Minitab 17 software. During two-way ANOVA, effect of recycled EPDM rubbers (R wt.
Data given in Table  4  indicated that recycled EPDM rubber content, AF content and the interaction between recycled EPDM rubber and AF have significant effects on ultimate flexural stress, flexural modulus and strain at break. After implementing the same procedure on AF content as well as the interaction between AF and recycled EPDM rubber content in the composites, significance of the constituent content on the mechanical properties was determined. Data given in Table  4  indicated that recycled EPDM rubber content, AF content and the interaction between recycled EPDM rubber and AF have significant effects on ultimate flexural stress, flexural modulus and strain at break. fibers more precisely. As two different fillers were used, it was necessary to use the H-T equations twice  [13, 14].
DIC technique The Digital Image Correlation (DIC) technique was first introduced for 2D images by Sutton et al. [41]. This technique initially aims at measuring the full in-plane deformation of an object. For that purpose, two monochromatic images are obtained from this object (a reference picture and a deformed picture). Images are defined as 2D matrices where each value corresponds to a grey level amplitude. The matrices can be cropped to discard irrelevant data and in such cases, the remaining region is called Region Of Interest (ROI). The images or ROIs are then compared using a correlation algorithm. The algorithm initially proposed by Sutton et al. used a spatial 2D cross-correlation product. Under the hypothesis of grey level conservation, the cross-correlation product is maximum when the two images match.
The contact between the rod and the sample is ensured using a spring. Because of the small stiffness of the spring (1 N/mm), the stress induced by the rod is neglected. The conditioner used to obtain 2 is an operational amplifier integrator circuit  [51, 52]. Integrating capacitors of 100 nF to 2000 nF are used to measure the sample response under electric fields from 10 V/mm to 4 kV/mm. The sample elongation ∆ mean image resolution to be maximised and ensures the angle between the sample surface and the camera sensor plane to be less than 0. 2°. Once images are acquired, they are correlated in a post-processing step with the 2D-DIC MATLAB program Correli RT3  [40, 42]. The displacement and strain fields (longitudinal and transverse) of the sample are then obtained. All DIC computations are made using the regularisation lengths 0 = 2 × 0 = 400 px.
e. after 72 hours. When the amount of PFA increases, the hydrophobicity of composites is improved. This phenomenon has been also observed by Deka and al. with kenaf biofibers  [24]. The water absorption is slower in cork granules. A kinetic analysis was performed to evaluate the rate constant (k) for each set of data. The data were selected between the lowest and the highest value of water absorption and normalised to compute an extent of conversion (α) relative to the increase of water absorption. A non-linear fit was applied to the data using OriginPro 8. 5 to evaluate the kinetic parameters according to the following equation: = 1 - -1 + 1 (Eq. 2) where n represents the reaction order, corresponding to a fitting exponent in this case. The extent of conversion α experimental and simulated is represented as a function of the time in Figure  7b.
Positively charged fragments were extracted into the mass spectrometer 0. 05 μs after the electron pulse. Data acquisition was controlled by a PC running LabVIEW code. The electron energy was increased from 5 to 100 eV in 0. 25 eV steps, and at each energy a mass spectrum was acquired and added to the data already accumulated. After each scan of the electron energy (taking 220 min. ), the full data set was written to a file. The full data set used for this paper consists of 10 scans of the electron energy, and is a twodimensional array of ion yield as a function of flight time and of electron energy. During the heating up of the oven no changes in the relative peak heights (notably those of the parent ion and the strongest fragment ions at 87 u, 58-60 u, 44 u, 31 u, 28 u) were observed. Comparison of mass spectra taken before, during and after the data acquisition showed no sign of thermal decomposition or other undesired effects.
Acknowledgements The authors are indebted to  Dr. Minaxi Vinodkumar  for discussions. We are also grateful to  Irina Jana  and  Dr. Kate Nixon  for assistance and discussions linked to the MPI experiments. PvdB and MB acknowledge financial, logistical, and technical support from the  National University of Ireland Maynooth. SE, JB, AR, and MR acknowledge the  Open University 's financial, logistical, and technical support. SE acknowledges the support the  British EPSRC  through a  Life Sciences Interface Fellowship  ( EP/E039618/1 ), a  Career Acceleration Fellowship  ( EP/J002577/1 ), and a  Research Grant  ( EP/L002191/1 ). The  European Commission  is acknowledged for a  Marie Curie Intra-European Reintegration Grant  ( MERG-CT-2007-207292 ). SE and JCP are grateful to the  CNRS  for a  PICS grant  ( 07390 ) supporting the collaboration between CIMAP/GANIL and the OU.
Recent institutional and policy frameworks prescribe the incorporation of ecosystem services (ES) into land use management and planning, favouring co-production of ES assessments by stakeholders, land planners and scientists. Incorporating ES into land management and planning requires models to map and analyze ES. Also, because ES do not vary independently, many operational issues ultimately relate to the mitigation of ES trade-offs, so that multiple ES and their interactions need to be considered. Using a highly accurate LULC database for the Grenoble urban region (French Alps), we mapped twelve ES using a range of models of varied complexity. A specific, fine-grained (less than 1 ha) LULC database at regional scale (4450 km²) added great spatial precision in individual ES models, in spite of limits of the typological resolution for forests and semi-natural areas.
At local scales, incorporating ES in land management and planning requires spatially explicit data for mapping  ES (de Groot et al. 2010; Maes et al. 2012). Most ES models use Land Use and Land Cover (LULC) maps as primary proxy data  (Verhagen et al. 2016; Lavorel et al. 2017). Nowadays, several LULC maps are publicly available due to growing interests in spatially explicit data in numerous fields. Among available LULC maps, Globcover data (worldwide), Corine Land Cover (Europe) and the recently produced Land Cover CES in France (©CESBIO-Theia,  Inglada et al. 2017 ) are used most commonly. However such publicly available LULC maps are always produced at large spatial scale and despite their common use at local scales, their limited spatial resolution, typology and accuracy induce large uncertainties in ES mapping  (Eigenbrod et al.
2010). High resolution maps may also be available at local scales, but are usually restricted to a single type of LULC. For instance, in Europe the Urban Atlas is available for dense urban areas, and in France the Registre Parcellaire Graphique (hereafter RPG) is based on declarations of agricultural use by farmers to claim European agricultural subsidies. However, the specific typological information available in these databases makes their direct use for ES mapping impossible; furthermore the various objects represented in such bases are not congruent across databases (different scales, different resolutions etc. ), and their mapped areas are not necessarily contiguous or spatially exhaustive. Their use is therefore only possible in conjunction with other data sources in order to constitute a continuous and exhaustive LULC map at landscape or regional scale  (Vannier 2011; Hubert-Moy et al.
2012; Mathian and Sanders 2014). On the other hand, remote sensing is frequently used to produce LULC data meeting needs for local-scale mapping in terms of configuration and dynamics  (Hansen and Loveland 2012; Giri et al. 2013; Kuenzer et al. 2014). The best practice is therefore to merge all available databases (public and remote sensing-based) to produce a LULC map achieving sufficient precision both in terms of resolution and typology, in order to best capture local specificities. Merging heterogeneous data for mapping ES is thus necessary and can produce great contributions to a better spatial understanding of ES patterns  (Cord et al. 2017). But such an exercise may raise questions about the adequacy between available data at the desired scale and the complexity of local ecological or social processes that ES maps aim to capture  (Spake et al.
Second, based on these needs and on stakeholders' local knowledge, and given known limitations of broad scale bundle analyses  (Spake et al. 2017) , we analysed ES bundles within characteristic landscape types associated with contrasted socio-ecosystems of the region. Third, an unusually large set of 12 ES were modelled and analysed using stakeholders and scientists' expert knowledge, skills and data. To implement this original analysis of ES bundles, we: 1-used a very high resolution LULC database especially developed for ES modelling in the Grenoble region (French Alps); 2analysed the relations between the LULC maps and ES bundles within characteristic landscape types; 3-we concluded by identifying contributions and limits of this approach to propose targeted areas of progress for future studies.
Three mountain ranges shape the area (Vercors, Chartreuse and Belledonne), with natural and semi-natural landscapes benefitting from numerous protection measures (Natural Regional Park, conservation areas etc. ). Overall, the Grenoble employment catchment area includes 311 municipalities within a 50 km radius, structured in ten EPCI: Grenoble metropolitan area, South Grenoble, Grésivaudan and Voiron urban area (which together constitute the Grenoble 'Y', thus called due to its shape on a map), Chartreuse, Vercors (the two mountain ranges outside the SCoT perimeter), Trièves, Matheysine South Grésivaudan, Bièvre-Valloire (plains and plateaus dominated by agriculture). Vannier et al. (2016)  and  Lefebvre (2014)  have described in detail the data sources and methods used to build our LULC database (Table  1, levels 1, 2, 3 ).
In short, we assembled, trimmed and cleaned the public databases from IGN 2 'BD Topo', Urban Atlas and RPG, and pre-processed and projected RapidEye and MODIS remote sensing data on the geometry of IGN 'BD Ortho'. These data sets were merged, segmented and then photo-interpreted to correct typological errors and complete uncharted areas in public databases. We thus produced a spatially accurate database, with a typology adapted to local needs (Table  S1 ). An independent photo-interpreter validated the resulting maps.
LULC database In order to further refine the description of agricultural land use (Table  1 , level 4), we used medium resolution remote sensing data from MODIS (Moderate Resolution Imaging Spectroradiometer). Classification and validation methods are described in  Lasseur et al. (2018). In short, we classified images based on the phenological properties of the different crop types and using altitudinal and climate information. This allowed us to produce a map of the major crop types (winter, spring or grassland) at an annual time step between 2008 and 2012. Aggregated over 5 years, these maps supported the identification of prevalent crop successions in the study area. These maps were validated with RPG data, producing an overall accuracy index (kappa index) varying between 0. 78 and 0. 82 depending on the year.
This crop succession map was integrated into our overall LULC map. The resulting database has a 10 meters spatial resolution over a 4450 km 2 extent, with a minimal object size of 0. 01 ha, i. e. , 100 m 2 , and a positional accuracy of 5 to 10 meters. The typology of this database includes 34 classes of land use/land cover (Supplementary material S1), nested into four levels of precision. These encompass built-up areas (residential and commercial/industrial areas are distinguished, as well as road and railway networks, etc. ), agricultural lands (specifying crop successions, grasslands, garden marketing plots or orchards). Forested and semi-natural areas are described by stands. The level of description of this typology is more detailed than what is available in most public databases, which corresponds to levels 2-3 of our typology, depending on land-use type (level 1).
ES bundles analysis ES bundle analysis had several objectives: 1-to produce an inductive, spatially-explicit classification of ES within each of the three landscape types; 2-to identify specific bundle-providing LULC classes by analysing relations between such a classification and the LULC map. ES bundle analysis consists in identifying ES associations within a given area (Raudsepp-Hearne et al. 2010 ), thus partitioning the study area into internally-consistent spatial units in terms of ES supply  (Spake et al. 2017). Such analysis aims to highlight similar areas in terms of ES levels (weak/ high values for the same ES), and ES most distinctive of each spatial unit. After normalizing ES maps (following  Paracchini et al. 2011) , we performed an unsupervised classification using the ArcGis software (IsoCluster tool, ESRI Inc, Redlands, CA, USA).
ES maps Given that the LULC map was used as a basis of nine out of the twelve ES modelled, that seven out of the twelve ES models were proxy-based or phenomenological, it seems essential to first briefly describe the ES maps in relation to the information already provided by the LUCC database. Figure  2  presents the twelve ES maps produced. The complementarities of the different landscape types units of the study area are apparent from a perusal of these maps: mountain or plain areas are clearly distinctive when individual ES are considered, while each area produces one or more ES. As expected, ES spatial patterns are quite correlated with LULC spatial patterns. This results from: 1-a given LULC type being either favourable or not to a given ES, and 2-the LULC map being a predominant input in the considered ES model.
Given the relevance of ES assessment for planning and policy  (Haase et al. 2012; Queiroz et al. 2015; Locatelli et al. 2017) , and for addressing sustainability issues, analyses such as this one can strongly motivate stakeholder participation. In our study, the process of ES mapping was conducted through a participatory approach with managers and planners. They suggested analysing ES bundles within landscape types was, but once bundles were identified within each landscape type they often preferred to return to individual ES they felt more familiar with  (Brunet et al. 2018). However, this work also revealed a number of limitations discussed below: 1-the simplicity of the models developed/used (mostly proxy or phenomenological models) resulting from the large extent, the regional diversity of landscapes, data availability, available knowledge and expertise; 2-the limiting typology for forest areas which takes into account only the broad stand types, whereas structure and management that play a key role in the ES supply in mountain regions would be required in a more relevant, detailed typology.
At a landscape to a regional scale, our results thus supported the consideration of major land use specificities in the LULC map construction, here differentiating crops and grasslands, and their sequencing within cultural rotations. This is not always possible when using an existing LULC database, especially when developed for a European or a national scale. By zooming within landscape types, the contribution provided by the use of a very precise LULC database is even more important. The withinlandscape types analysis highlighted the role of LULC time sequences in agricultural areas, where in the periurban landscape, crops and grassland sequences have a major role in ES supply, whereas in the mountain rural landscape, the role of permanent vegetation (forest and grassland) play a key role in ES supplying.
But this landscape type analysis showed also a main limitation regarding the forest landscape type as the LULC database is unable to provide of good information on vegetation structure (open or close forests) and on management practices whereas production forests representing 89% of the forest areas of the study site. Even if the ES models specific to forest area (Wood production, Carbon Stock, Rockfall protection) take into account spatial data (land tenure, sylvo-ecoregions, forest flux inventory, see S3), the bundle analysis did not capture more specific features than the three major forest types (hardwood, coniferous, mixed forest). However, the prominence of LULC input maps in our models strongly constrained the identification of spatial patterns of ES bundles for the periurban and mountain rural landscapes.
Modelling ES at the landscape to the regional scale The ES modelling process was the result of coconstructed choices for ES and model selection, guided by: 1-our knowledge of the study area (field measurements and previous studies, empirical knowledge from local stakeholders and the scientific team); 2-data availability for model parameterization; and 3the extent of the study area (small region), along with a fine-scale resolution. Producing a specific LULC database at a precise grain (less than 1ha) and at the scale of a small region (4450 km 2 ) required a huge effort but added a considerable spatial information for modelling ES compared to other studies of the same type  (Haase et al. 2012; Crouzat et al. 2015; Queiroz et al. 2015). As in most other ES mapping studies, LULC data is the most important ES model input  (Seppelt et al.
2010). This was illustrated for recreation, where our phenomenological model combined multiple LULC and derived variables. Nevertheless for some ES models, the limited availability of specific data and knowledge was a great limitation. As a case in point, the InvEST model parameterization for regulation of water quality is based on literature values for main land cover types  (Cabral et al. 2016). While it is certain that specific crop successions have different nitrogen retention abilities, we were unable to take advantage of our knowledge on their types and spatial distribution due to the absence of data for that parameter. Data and knowledge availability further limited the capability of using models with intermediate to high process resolution, i. e. Tier-2 and -3 models  (Grêt-Regamey et al.
Avenues for improvement of ES modelling Recent studies have demonstrated how high resolution remote sensing data coupled with field data collection can support the identification and the quantification of a wide diversity of ecosystem states and functions  (Ayanu et al. 2012; Andrew et al. 2014; Cord et al. 2017; Vihervaara et al. 2017). Results from our study along with results from these works suggest that remote sensing has the potential to supply numerous data required by ES models. In particular, our work illustrates how remote sensing can be used to produce LULC maps tailored to the needs of ES models. Compared to publicly available LULC maps, the map used in our study enables a fine description of agricultural lands but remains very coarse concerning forest and semi-natural areas (respectively four and three classes for forest and semi-natural areas, compared to 18 for agricultural lands).
e. 8 out of the 12 ES modelled). LiDAR data is available for part of the study area's forests, and should be extended in the future, granted required financial resources. Beyond improvement of LULC maps, remote sensing brings numerous other opportunities to overcome the current lack of data in ES mapping. In particular, recent developments of satellite-based and drone-based sensors significantly increase the affordability of multi-temporal, hyperspectral and high resolution images as well as LiDAR data. This diversity of data opens up broad prospects to a direct mapping of ecosystem functions and services. Among most promising approaches, the scaling up of plant functional traits from field measurements to regional level  (Ayanu et al. 2012; Homolová et al. 2013; Andrew et al. 2014; Abelleira Martínez et al.
Acknowledgments This research carried out on the  LTSER Platform Zone Atelier Alpes contributes  to project  ESNET  ( Ecosystem Services NETwork  futures for the Grenoble region) funded by  ONEMA (Office national de l'Eau et des Milieux Aquatiques)  and  EU   FP7  project  OPERAs FP7-ENV-2012two-stage-308393.
Effect of harman and norharman on serotonin level in G. mellonella larvae hemocytes Three-day-old, last (seventh) instar G. mellonella larvae were given harman (H) and norharman (N) topically or mixed with food at final concentrations of 750 (1H, 1N), 1000 (2H, 2N) or 1250 (3H, 3N) ppm. Both alkaloids were also added to hemocytes cultured in vitro, and 5-HT immunolocalization was performed in all experimental groups. The level of 5-HT in whole hemolymph (containing homogenized hemocytes) was determined using ELISA. Raw data is presented in Additional file 1. The presence of 5-HT was found in the hemocytes of the control groups, with punctate fluorescent signals observed in single cells. Serotonin concentration was found to range from 0. 49 to 0. 69 ng/ml of hemolymph, and hemocyte concentration increased following the administration of harman and norharman to insects.
Effect of harman and norharman on phagocytosis Phagocytic activity was examined in hemocytes obtained from G. mellonella larvae 1 h and 24 h after topical application and 24 h after intake with food, as well as from control insects. This process was also tested in vitro after the addition of harman or norharman directly to cell cultures. To visualize the phagocytosis process, Escherichia coli (K-12 strain) BioParticles fluorescein conjugate was used. Phagocytic activity was determined by measuring the resulting fluorescence, as described in "Materials and methods" section. Raw data is presented in Additional file 2. Two types of hemocytes were visible in microscopic images: plasmatocytes and granulocytes. The remaining hemocyte subpopulations of G. mellonella i. e. spherulocytes, oenocytois and prohemocytes, are not adherent and were washed out during the fixation and staining procedures.
The cell nuclei were stained with Hoechst (Enzo Life Sciences). The hemocytes were observed under an Axio Vert. A1 fluorescence microscope (Zeiss) with Axio Cam ICc 5 (Zeiss) and ZEN lite 2012 software. The amount of 5-HT in G. mellonella larval hemolymph was also determined. Hemolymph from the treated larvae was collected as described above. The concentration of serotonin was investigated also in hemocyte cultures in vitro. After incubation with alkaloids, cells were detached by trypsin-EDTA solution 0. 25% (Gibco), and the cell suspensions were transferred to sterile polypropylene 1. 5 ml centrifuge tubes. The samples were homogenized by sonication and centrifuged at 250×g for 10 min at 4 °C. The supernatants were stored at -80 °C. The level of 5-HT present in the supernatants was determined using a commercial ELISA kit produced by Labor Diagnostika Nord (LDN), (Germany).