dataesr/scientific-paragraphs-categorization huggingface dataset:
Bacterial transformation was performed in One Shot TM TOP10 Chemically Competent E. coli and spread on LB plates with 50 µg/ml ampicillin. MdPDS and TFL1. 1 insertion colonies were checked by PCR (primers 19 and 20) using GoTaq R Flexi DNA Polymerase with pJET 1. 2 primers (Table  1 ) using conditions described in part 2. 4. The bacteria containing putative edited sequences were directly sent for sequencing. All sequencing results were compared with the reference sequence of the wild type apple MdPDS (MD04G0021400) or TFL1. 1 (AB052994) genes and pear (PCP025869. 1) TFL1. 1 gene by alignment in MultAlin software  (Corpet, 1988). Putative off-target genes for gRNAs in our constructions were obtained thanks to CRISPOR software 4   (Haeussler et al. , 2016). For mutation analyses in putative off-target genes, strategy was as before: genes primers pairs  (21 and 22, 23 and 24, 25 and 26, 27 and 28 ; Table  1 ) were designed based on genomic sequences from the apple 'Golden Delicious' genome  (Daccord et al.
Analysis of Potential Off-Target Mutations Potential off-target sequences were identified using the CRISPOR software. We found no off-target genes for gRNA2 in CRISPR-PDS construct and two for gRNA1 with three mismatches each. In CRISPR-TFL1. 1 construct, we found two putative off-target genes for each gRNA. gRNA1 could target MD12G1023900 and MD14G1021100 with, respectively, zero and three mismatches and gRNA2 could target MD14G1021100 and MD01G1193900 with three mismatches each (Table  4 ). Given the greater probability to affect off-target genes with CRISPR-TFL1. 1 construct than with CRISPR-PDS construct, we analyzed only putative off-target genes of CRISPR-TFL1. 1 construct. Two to four bacterial clones were sequenced for each off-target gene in five different transgenic lines (CRISPR-TFL1.
ACKNOWLEDGMENTS The authors sincerely thank  F. Nogué  ( INRA Versailles ) for his assistance in vector design and cloning strategy. The authors also thank  M. Mazier  and  B. Danilo  ( INRA Avignon ) for providing the plasmid pDE-CAS9Kr. The authors gratefully acknowledge the technical platform ANAN (SFR 4207 QUASAV) and  L. Ogé  ( IRHS-GDO ) for his helpful advice on gel purification.
Then, the system is shortly relaxed by integration in the isothermal-isobaric ensemble. The final result of this procedure is an initial relaxed GB configuration. In our simulations, we have always used a precipitate diameter smaller than half the smallest dimension of the simulation box to minimize the interactions with periodic images. To perform LD simulations, we implemented Eq. (  8 ) in a numerical code written in Fortran that makes uses of a linked-bin algorithm combined with a neighbor list to calculate interatomic forces, as usual in molecular simulations. We use the reduced time τ = t LJ ν and, for the time integration we use the Heun's method discussed in Appendix. In this paper, as we only consider simulations at zero pressure, all the components of the Piola-Kirchhoff tensor P are set to zero.
To perform MD simulations we used the open-source code LAMMPS  (Plimpton 1995)  with a Nose-Hoover style thermostat and barostat  (Nosé 1984; Hoover 1986). To be able to characterize grain boundaries, we performed a Delaunay triangulation in the computational geometry to find the number of nearest neighbors (NN) of each atom. In a perfect triangular crystal, each atom has 6 NN. Along grain boundaries, we observe the formation of topological defects that are defined as particles that do not possess six NN. In our simulations, we found the occurrence of pairs of particles having 5-7 NN that we interpret as dislocations or structural units depending on they are isolated or not.
A thin layer of silver paste was painted to cover the InGa eutectic contact as well as a part of the metal wire. After drying of the paste, epoxy resin (Loctite 9460, Henkel) was deposited to shield the backside and frontside of the surface except an active area comprised between 0. 1 and 0. 25 cm 2 , note that the exact geometrical value was precisely measured using the ImageJ software prior to the (photo)electrochemical experiments. The electrodes were baked in the oven at 90 °C for 1. 5 h to cure the epoxy resin. Preparation of the NiFePB coatings. The electrodes were electrochemically modified with NiFePB using a freshly-prepared 5 mM K 3 Fe(CN) 6 electrolyte (pH adjusted to 2. 5 with concentrated HCl). We have tested different electrochemical methods that gave satisfactory results in terms of modification, however, the pulse method was found to give the deposit with the highest catalytic activity for urea oxidation.
The slot was further thinned using dimpling grinder (Model 200, Fischione instruments) down to a 10μm-thick target area before being further processed onto an ion milling machine (low angle ion milling and polishing system, Model 1010, Fischione instruments) to reach a final thickness of <100 nm for TEM analysis. TEM and scanning transmission electron microscopy (STEM) were performed with a JEM 2100 LaB 6 (JEOL) equipped with a Silicon Drift Detector (SDD) -X-Max (Oxford Instruments) and the AZtec software to perform energy-dispersive X-ray spectroscopy and elemental mapping. XPS measurements were performed with an Mg K alpha (hν = 1254. 6 eV) X-ray source and an Al source (hν = 1486. 6 eV) using a VSW HA100 photoelectron spectrometer with a hemispherical photoelectron analyzer, working at an energy pass of 20 eV for survey and resolved spectra.
Acknowledgements This work is funded by  ANR  (project  EASi-NANO ,  ANR-16-CE09-0001-01 ). We acknowledge  ScanMAT/THEMIS for TEM and Loïc Joanny from ScanMAT/CMEBA  for SEM analyses. Thierry Labasque  ( Geosciences Rennes ) and  Nina Hüsken  are warmly acknowledged for valuable discussions.
2013; Murgue et al. 2016). Recent improvements in satellite imagery, the increased availability of harmonized censuses and surveys at large scales have contributed to the development of spatially explicit cropland databases. For example, remote-sensing-based databases  (Bégué et al. 2015; Waldner et al. 2015a ) and periodic surveys such as the European Land Parcel Identification System (LPIS)  (Sagris 2013) , the LUCAS database  (Temme and Verburg 2011) , the French TerUti database  (Xiao et al. 2014) , and the Farm Accountancy Data Network  (Vitali et al. 2012)  provide improved support for describing and modeling crop sequences. The generic character of these databases allows using them for many purposes in different domains and at different levels of analysis  (Murgue et al. 2016). However, they have a major limitation: the static perspective adopted for crop management systems is not appropriate for analyzing the dynamic interactions between technical operations, weather conditions and natural resource conditions  (Ruiz-Martinez et al.
2015; Murgue et al. 2016). Studies based on these data have addressed the crop management component of cropping systems only loosely  (Martínez-Casasnovas et al. 2005; Mignolet et al. 2007; Steinmann and Dobers 2013; Sahajpal et al. 2014; Kollas et al. 2015) , for example representing average practices  (Therond et al. 2011)  or fixed-calendar scheduling  (Dupas et al. 2015). Overall, we identify three major obstacles that limit a complete description of cropping systems with such databases. First, available land cover maps provide insufficient spatial continuity and temporal depth to properly describe crop sequences  (Fuchs et al. 2013; Xiao et al. 2014). Second, a lack of information about crop management exists; it is generally addressed by using land cover as a rough proxy (e. g. ,  Verburg et al.
2005; Leenhardt et al. 2010). To describe the spatial variability and dynamics of cropping systems, it is critical to enrich the information provided by generic data sources with local knowledge obtained mainly through interviews, surveys or local databases  (Marie et al. 2008). Local knowledge is crucial to capture information related to farmers' decisions, i. e. location factors and crop management decision rules. This knowledge is largely implicit, however, and consequently difficult to access  (Aubry et al. 1998; Tress et al. 2003; Toffolini et al. 2015; Murgue et al. 2016). Interviews with farmers remain the main approach for obtaining this knowledge  (Marie et al. 2008; Debolini et al. 2013; O'Keeffe et al. 2015). However, an exhaustive survey of all farmers in a region can easily become excessively time-consuming (see examples in  Maton et al.
(2016). The second area (1 446 km²) is located in the upstream area of the Adour River, which drains the northern slopes of the French Pyrenees. Recent statistics highlight that it has the highest irrigation water withdrawals in the Adour River basin (OEBA 2015). Its climate is oceanic, with relatively warm and humid weather in summer and heavy rainfall events in winter. The two main soil types in each study area are (i) alluvial and hydromorphic loamy silt soils, mainly associated with fluvial terraces, and (ii) calcareous clayey soils, which predominate elsewhere (Fig. 1 A  and B ). As for any area in France, generic databases are available to map the main attributes of the study areas  (Murgue et al. 2016). For example, the "Land Parcel Identification Systems" (LPIS) is a georeferenced database to be developed by each member state of the European Union to control annual Common Agricultural Policy (CAP) subsidies.
It stores the land cover information (nature and area) of each CAP islet of each farmer concerned by these subsidies  (Inan et al. 2010). We used the French LPIS database  (for 2006-2012)  to (i) map the main annual crops and spatial distribution of fields (ASP 2012), (ii) retrace crop sequences at the field scale, and (iii) identify fields that were declared as irrigated at least once from 2007-2009. Of note, LPIS no longer collected irrigation-related data after 2009. The French LPIS mapping unit is a "farmer's block" or "Common Agricultural Policy islet", which corresponds to one or more aggregated contiguous agricultural land parcels  (Sagris 2013; Murgue et al. 2016). We also used the CORINE Land Cover database for 2012  (EEA 2015)  to complete the land-use map for orchards, typically underrepresented in LPIS, and to map non-agricultural land cover (i.
e. , forest and urban). Representing 55% of the total area, arable land dominates in both study areas, but includes different percentages of irrigated area: 28% in the Tarn area and 39% in the Adour area (Table  1 ). Additional information about cropland composition was obtained from the general agricultural census performed by the French Ministry of Agriculture (Agreste 2010) and freely available at the Local Administrative Unit 2 level, corresponding to a French municipality (Eurostat 2015). We obtained climate data from the SAFRAN database  (Vidal et al. 2010 ) and soil types from the French Geographic Soil Database at a scale of 1:1 000 000, which represents the French portion of the European Geographic Soil Database  (King et al. 2005; Antoni et al. 2007). Finally, we used water withdrawal data provided by water users through an annual mandatory report to the Adour-Garonne Water Agency (AEAG 2015).
Modeling cropping systems and their spatial distribution Based on the information collected from interviews, we modeled spatial distributions of cropping systems in each study area in three steps. First, we addressed the two components of cropping systems, namely the crop sequences (step 1) and the decision rules of crop management (step 2). Then, we coupled the dynamic decision rules with the location factors (step 3). Step 1. Describing crop sequences using real data Our objective was to define sequence types to infer irrigation management rules. First, we processed the 2006-2012 LPIS data (simplifying the crop labels slightly to perform an initial grouping). This allowed us to identify the real crop sequences observed in all farmers' blocks (see details in  Leenhardt et al. 2012). Since this list of sequences was too large to manage easily, we created a typology of crop sequences useful for dealing with crop management decision rules.
The dominant agricultural soil type of each farmer's block from the 1:1 000 000 soil database was assigned to each of its land parcels. If the original LPIS crop class observed on a given land parcel was divided into new classes during step 2, one of these new classes was assigned to the land parcel according to location factors such as the soil type, crop sequence or crop area of the farm it belonged to, while respecting, for each study area, the proportions of these new classes observed in the 2010 agricultural census (Agreste 2010). We assigned the crop sequence of each land parcel to a crop sequence type (defined in step 1) using the Dice percentage similarity index  (Dice 1945). Irrigation status of a land parcel was determined using 2007-2009 LPIS data. We assumed that land parcels were potentially irrigated, i.
Evaluating the approach Evaluating our approach was challenging: because it was developed to compensate for the relative lack of cropping system data, it used all the data available to formalize and spatialize the cropping systems, leaving no data for classic "observed vs. simulated" evaluation. We therefore evaluated the approach indirectly. We used the cropping system information retrieved from our approach to parameterize a model able to simulate irrigation withdrawals and compared simulated withdrawals to withdrawal data obtained from the Adour-Garonne Water Agency (AEAG). Since AEAG data were annual data only, we also verified that, for each cropping system, the simulated withdrawal dynamics were consistent with key informants' descriptions of irrigation starting and ending dates. Irrigation withdrawals were chosen as an evaluation indicator because the simplified survey of crop management practices focused on irrigation and only secondarily on sowing and harvesting.
The model used was chosen for its ability to account for all components of the cropping system: the observed crop sequences retrieved from the generic data sources, the decision rules and the location factors identified from the key informant interviews. This indirect way to evaluate the reliability of a modeled spatial distribution of cropping systems was previously used by  Murgue et al. (2016). For simulating water withdrawals, we used the agricultural module of the MAELIA multiagent and spatially explicit simulation platform  (Gaudou et al. 2013; Therond et al. 2014; Mazzega et al. 2014 ; http://maelia-platform. inra. fr/). This agricultural module, fully described by  Murgue et al. (2014) , combines a soil-crop model and a farmer agent model. The former predicts daily soil water balance based on rainfall, evapotranspiration, soil water content and crop development stage  (Constantin et al.
2015). The latter applies farmers' decisions using crop management decision rules. Virtual farmer agents check, every day and for each land parcel, whether thresholds encoded in management rules have been reached or exceeded and, if so, perform the corresponding technical operation(s). Note that since the hydrological and normative modules of MAELIA were deactivated, virtual farming agents have access to unlimited water resources. The modeled cropping systems, i. e. observed crop sequences purposely simplified (step 1), along with the decision rules (step 2) and the location rules (step 3), were used to parameterize the agricultural module of MAELIA. Through the multi-agent structure of MAELIA, this module (soil-crop and farmer agent models) was run independently for each land parcel in each farmer's block on each farm in each study area.
During a simulation, the model reads through the crop sequence each year to find the crop grown, then associates the crop with its management system (i. e. , the set of IF-THEN rules) as a function of the crop sequence type, soil and irrigation equipment, based on the table constructed for this purpose. The simulation ran from 2003, an extreme drought season, to 2012, thus covering the period for which LPIS data were available to reconstruct crop sequences  (i. e. , 2006-2012). Crop sequences observed from 2006-2012 were replicated to cover the previous years without LPIS  data (2003-2005). Given the many fields simulated (Table  1 ) and their multiple interactions, our first objective was to reproduce the interannual dynamics of total withdrawal recorded in the AEAG database.
2014). The ability to obtain extensive coverage of the study areas from only a few interviews was facilitated by the fact that French extension services are generally organized to operate with relatively few agents. In this regard, the help of the two managers was crucial. The design of targeted intermediate objects (maps, tables and diagrams) also helped structuring the interviews to obtain the missing data in the two study areas. When evaluating this approach with MAELIA's agricultural module, we observed a similar ability to simulate water withdrawals as in the previous study in the nearby Aveyron watershed  (Murgue et al. 2016). Our approach, using local knowledge of key informants to formalize location and crop management rules, performs as well as the previous approach: the same order of magnitude of differences between simulated water withdrawals and AEAG withdrawals in the present study as in  Murgue et al.
(2016). Besides, our simplified approach is more efficient since it enabled us to describe crop management systems at the regional scale on six times as many fields as in the Aveyron study  (Murgue et al. 2016) , in approximately one-fourth of the time required in this previous study. Limits. The limits of our approach are closely related to the quality of the generic data and the local knowledge used. First, the irrigated status of land parcels appears as probably poorly estimated, as revealed by the underestimates of simulated water withdrawals. Indeed, we used 2007-2009 LPIS data to determine whether land parcels could be irrigated, which might have biased estimates of irrigation status before and after this period. In this perspective, it would be reasonable to update these data using specific sources, such as recent multi-sensor, highresolution time series of satellite images being evaluated for identifying irrigated crops in our study region  (Waldner et al.
However, it has a generic character. First, it uses data that is relevant beyond these two study areas. The LPIS database, from which we obtained descriptions of crop sequences, is increasingly used to map crop patterns and dynamics in France  (Durpoix and Barataud 2014; Rizzo et al. 2014; Bouty et al. 2015; Levavasseur et al. 2016 ) and elsewhere in Europe  (Leteinturier et al. 2006; Levin 2013; Zimmermann et al. 2016). Second, the local information was integrated using intermediate objects (e. g. , diagrams) and then formalized as IF-THEN rules for management decisions and cropping system location. Combining diagrams and IF-THEN rules defined a methodological framework that can be adapted easily to study other areas and crop management practices besides irrigation, as well as to account for various annual and perennial crops.
We found that the intermediate objects prepared for the interviews (scripts and diagrams) could be adapted rapidly from the first area (Tarn) to the second (Adour), demonstrating their genericity. Formalizing the knowledge that underpins the intermediate objects used for key informant interviews may eventually facilitate social learning among farmers, extension agents and scientists, at least by providing focal points for communication and bridging gaps among them  (Jakku and Thorburn 2010; Houllier and Merilhou-Goudard 2016; Moonen et al. 2016). More generally, we assume the key informant approach can be repeated anywhere, and also extended to the collection of other data, e. g. of crop rotations and several technical operations, where or when generic database are not available, as did  Mignolet et al.
(2004). Though, its main limitation, as in other participatory approaches, is the difficulty in identifying the best key informants. One way to evaluate their representativeness is by characterizing the local web of actors and the socio-technical system  (Mathevet et al. 2014). We limited our key informant approach to extension agents, but other local actors have knowledge about crop management practices that complements farmers' knowledge. During the interviews, managers of local water-user associations, who are also farmers, or extension agents working for private or crop-specific technical institutes, were often mentioned as able to provide relevant knowledge to describe cropping systems and irrigation practices at the regional scale. Evaluation. More complete evaluation of our approach would have been based on an independent dataset of observed farmers' practices.
This dataset would have included all (or nearly all) farms within the region to address the high heterogeneity of farmers' strategies. A dataset corresponding to a sample cannot represent the entire population and is thus insufficient for complete evaluation. However, no exhaustive dataset was available. The lack of adequate data is a major obstacle in evaluating model outputs, as often reported for model evaluation at the regional scale (e. g. Leenhardt et al. 2016; Temme and Verburg 2011; Waldner et al. 2015a ). Due to the lack of independent data on observed farmers' practices, we could not evaluate our approach directly; thus, we used a simulation model to assess its ability to provide consistent estimates of water withdrawals. We used AEAG water withdrawal data as a validation dataset despite its weakness in spatial coverage, locations of withdrawal points and how withdrawn volumes were surveyed over time.
Implementing the key informant approach allowed us to model irrigation decisions and their spatial distributions and, through dynamic simulations, to predict annual amounts and interannual dynamics of irrigation water withdrawals relatively well. One main innovation of our method was to use knowledge from generic databases when interacting with key informants and ad-hoc intermediate objects to help them focus on missing information. In particular, use of diagrams reduced interview duration and processing time. These intermediate objects are easy to reuse in new study areas. This approach was applied to two large areas and can easily be adapted to other areas where LPIS data are available. Although we focused on irrigation management practices in areas with water scarcity issues, this approach is sufficiently generic to be applied to areas, and can be easily adapted to focus on other management practices and crops.
Acknowledgments This study is part of the  SIMULTEAU  project, funded by the  French Ministry of Agriculture  as part of the  CASDAR program , and received additional funding from the  French National Office for Water and Aquatic Environment (ONEMA). The authors thank the interviewees from the extension services and the other local experts who generously contributed to the description and formalization of cropping systems for the Tarn and  Adour areas. Members  of the  SIMULTEAU  project are also acknowledged for their useful comments and improvements on the method. Finally, we thank  Pierre Casel  and  Denis Baudet  ( INRA UMR AGIR ) for their help in retrieving and processing the LPIS and geographic data.
Aer that, the melt was washed with distilled water and ltrated in order to remove easily the residual salts. The resulting powder was dried at 110 C for 3 h and heated at 800 C for 6 h. Finally, the powder was pressed into disks under 5 tonnes per cm 2 and sintered at 800 C. In order to characterize our samples we carried out, at room temperature, the phase purity by powder X-ray diffraction (XRD) using a Panalytical X pert Pro-diffractometer with Cu-K a radiation (l ¼ 1. 5406 Å). Data renement was obtained with 2q from 10 and 100 with a step size of 0. 017 and a step time of 18 s. The microstructure was determined, at room temperature, by a scanning electron microscope (SEM) using a Philips XL30. The compositions of our samples were obtained by a semiquantitative analysis performed at 20 kV accelerating voltage using energy dispersive X-ray analysis (EDX).