dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Statistical analyses were computed using the version 3. 6. 3 of the R software  [41]. Some outliers (within-plot replicate values) were observed in the PC and Nem sets and were removed. Within-plot replicate values were considered as outliers if they were out of the boxplot (lower than (Q1 -1. 5*Interquartile Range) or higher than (Q3+1. 5*Interquartile Range)) for, at least, two indicators in the same set. In total, 16 values (only 2. 7% of the data set) were removed from PC and Nem dataset. This data pre-processing was especially needed because of high data-set variability linked to the integration of inner-replicates in the analysis. For Biofunctool®, no outlier were removed as  [22]  already computed outlier analysis. First, univariate analysis was implemented and each indicator was studied separately using a linear-mixed effects model (package lme4  [42] ).
The number of variable per set of indicators was different with seven variables for Nem, nine for PC and ten for Bio-functool®. This difference is too weak to explain eventual discriminatory potential between sets. Indeed, with sets of seven to ten variables, discriminatory differences will be more linked to variable sensitivity to land management and non-redundancy, than variable number in multivariate analysis. To provide a comparison of the sensitivity of the three sets of indicators to the different land management, three soil quality indexes (SQIs) using separately PC, Nem and Biofunctool® dataset were calculated following  [22]. The soil quality index (SQI) is a statistical method allowing the aggregation of several indicators into a single quality score based on weightings derived from multivariate analysis (PCA)  [46].
Case of physico-chemical indicators (PC) Among the soil quality datasets tested, the PC set showed the least potential ability to differentiate the land management systems. PC was able to highlight significant differences only between forest and the cash crops (cassava and rubber). Carbon content and soil nutrients (Ctot, Ntot, Ca and Mg) were the main variables that explained these differences. These results are in line with a panel of others studies that highlighted a decrease in soil chemicals between forest, mature rubber tree plantations  [49] [50] [51]  and intensive annual cropping systems  [52]. Other chemicals (e. g. P and K) were rather linked to inter-plot variability, which was also observed by  [53]  for P content. This could be explained by variability of fertilization practices among smallholders  [54].
A useful compromise in the assessment of soil quality that has been poorly applied so far is to focus on functional indicators of soil quality that integrate interactions between soil abiotic and biotic compartments. The Biofunctool® functional indicators study three key soil functions: carbon transformation, nutrient cycling and structure maintenance, to describe soil complexity. No specific expertise is required to apply the indicators that are easily implementable. However, this integrative functional approach does not allow the examination of the soil processes behind soil functions and is currently little used as a diagnostic tool, compared to PC approach. Biofunctool® also remains a comparative approach for use in a specific context, and would require additional databases to provide absolute soil quality scoring such as current physico-chemical  [71] , or biological indicators  [72].
Acknowledgments This study was financially supported by the  LMI LUSES (Dynamic of Land Use Changes and Soil Ecosystem Services) , the  French National Research Agency (ANR)  in the framework of the  Heveadapt  project ( ANR-14-CE03-0012 ), the  French Institute for Natural Rubber (IFC) , the companies SIPH, SOCFIN and  MICHELIN  within the program " Hevea-Biodiv ". Pusanisa Heepngoen PhD  financial support was provided by the  Royal Golden Jubilee Ph. D. Program  (Grant number  PHD/0136/2557 ). Our special thanks go the  Chachoengsao Rubber Research Centre (Pisamai Chantuma) , the  Land Development Department in Bangkok  for all the support provided and particularly to  Nopmanee Suvannang  and  Audjima Phongjinda  for soil physico-chemical analyses. We are also very grateful to the farmers who allowed us to sample their plots and to all the people involved in the field experiments, especially  Philippe Thaler ,  Nitjaporn Koonklang ,  Leanghak Bun  and  Sreymom Sieng.
Effects of OWPs on soil enzymatic activities have been studied widely, but long-term effects remain poorly understood, and no information is available about differences in dynamics among systems for each biogeochemical cycle. The data described here were collected during two trials from an initial state, and they allow assessment of long-term effects of OWP application, mineral nitrogen fertilization, tillage and vegetation cover on soil enzymatic activities. Data are presented for the activities of five soil enzymes measured from 2012 to 2019: β-glucosidase, phosphatase, urease, arylamidase and arylsulfatase. Five additional enzymes were included in 2019 to supplement the analysis of biogeochemical cycles: alkaline phosphatase, phosphodiesterase, α-glucosidase, β-galactosidase and n-acetyl-glucosaminidase.
These activities were measured in two trials at the EFELE study site: PROs (five OWPs applied to a corn-wheat rotation) and TS/MO (four treatments that examine interactions between OWP and type of tillage). These data can be used as a reference for future studies of soil enzymes in France and other regions (e. g. for developing reduced-tillage systems and organic or inorganic amendments, and to assess dynamics of the systems). ©.
Value of the Data • This dataset is based on high-frequency temporal acquisition to detail differences in dynamics enzymatic activities in agricultural soils subjected to different soil management practices. • Communities such as agronomy, agricultural technical institutes and mathematical modellingcan benefit from these data to calibrate and design optimal agricultural practices. • These data can be used in meta-analyses to quantify effects of repeated inputs of OWPs, tillage and crop rotations on organic matter dynamics and changes in soil quality. • Data can be used to design statistical models to predict or evaluate effects of different treatments on soil enzymatic activities. • Data can be used to develop a vector analysis model of enzymatic activities. This model has been suggested as a good indicator of soil resource limitation  [2]  and reflects microorganisms' acquisition of C/N/P and nutrient acquisition effort.
Data Description This article includes descriptive statistics of the two trials, and figures that show effects of OWPs, catch crops, tillage and their combined effects on changes in soil enzymatic activities at the EFELE site. Data have been collected every year since the trials began in 2012 (i. e. 8 years of data currently available). Tables show the main soil physico-chemical properties of the surface horizon in the 48 plots of the two trials at the beginning of the experiment (March 2012) ( Table  1  ), descriptive statistics of soil activities in the PROs trial ( Table  2  ) and TS/MO trial ( Table  3  ), and the contents of the three dataset files ( Table  4  ). Figures show the map of trial plots at EFELE ( Fig. 1  ), principal component analysis of enzymatic activities of the PROs trial in 2013 and 2019 ( Fig.
2  ), mean enzymatic activities of OWP treatments relative to that of the unfertilized control in the PROs trial from 2012 to 2019 ( Fig. 3  ), dynamics of enzymatic activities of four treatments in the PROs trial from 2012 to 2019 ( Fig. 4  ), effects of plant cover and fertilization on enzymatic activities in the PROs trial from 2012 to 2019 ( Fig. 5  ), effects of reduced tillage on enzymatic activities in both soil horizons in the TS/MO trial from 2012 to 2019 ( Fig. 6  ) and effects of cattle manure or mineral fertilization on enzymatic activities in the 0-15 cm horizon with reduced tillage in the TS/MO trial from 2012 to 2019 ( Fig. 7  ). The dataset is composed of three Excel files that contain raw data ( Table  4  ). It includes data on enzymatic activities; mineral N, P and K applications; OWP application rates and composition.
Acknowledgements Biochem-Env ( https://doi. org/10. 15454/HA6V6Y  ) is a service of the " Investment d'Avenir'' infrastructure AnaEE-France , overseen by the  French National Research Agency (ANR)  (  ANR-11-INBS-0 0 01  ). The EFELE field experiment forms part of the  SOERE PRO (network of long -term experiments to study impacts of recycling organic waste products) certified by  ALLENVI (Alliance Nationale de Recherche pour l'Environnement ) and integrated as a service of the '' Investment for future'' infrastructure AnaEE-France , overseen by the  French National Research Agency  (  ANR-11-INBS-0 0 01  ). We thank  ADEME (French Environment and Energy Management Agency)  for its financial support. We are grateful to  F. Gaillard  and  P. Germain  for technical assistance. The authors thank  Michael Corson  for proofreading the manuscript.
Point mutation in the NLR OsRLR1 induces spontaneous HR-like lesions From a rice mutant library subjected to EMS treatment, we identified a recessive mutant, rlr1 (previously designated lmes2), that developed HR-like lesions on the leaf blade with an early senescence phenotype in the absence of pathogen infection  (Xing et al. , 2016). We previously mapped the mutation in a 66-kb interval on chromosome 10  (Xing et al. , 2016). We sequenced all genes in this interval and found a single-base transversion (A to T) at the 953 bp site of the coding sequence in LOC_Os10g07978, causing an amino acid change from Glu to Val at position 318 in the protein (Figure  1a ). The single exon of LOC_Os10g07978 encodes a 923 amino acid protein. A BLASTP search of the NCBI database (https://blast. ncbi. nlm.
A 3484 bp sequence of the OsRLR1 promoter was cloned in transcriptional fusion with the GUS gene. GUS staining of ProOsRLR1:GUS transgenic plants shows that OsRLR1 was mainly expressed in the vascular bundles of the leaf blade (Figure  S1h, i ). The mechanism of OsRLR1 mediated disease resistance in rice 1053 When OsRLR1-GFP and nuclear marker-RFP were co-expressed in rice protoplasts, the green and red fluorescent protein (RFP) signals were superimposed (Figure  1m ). Western blot showed that the OsRLR1-GFP fusion protein was in the nuclear fraction after cell fractionation (Figure  1n ). These results indicate that OsRLR1 exerts its biological function in the nucleus. A multiple sequence alignment of OsRLR1 and reported CLRs from rice, Arabidopsis, tomato, potato, maize and wheat was generated (accession numbers are listed in Table  S2 ).
OsWRKY19 activates expression of OsPR10 To test the transcription activity of OsWRKY19, we used the luciferase (LUC) reporter system in rice protoplasts and found that expressing OsWRKY19-GAL4 BD enhanced LUC activity by a factor of more than 5 compared with the empty vector pGreenII800, which indicates that OsWRKY19 functions as an active transcription factor (Figure  7a ). Rice protoplasts that expressed OsWRKY19 with ProOsPR10:LUC demonstrated enhanced LUC activity by a factor of 6 compared with the control transformed with ProOsPR10:LUC and the empty vector (Figure  7b ), which indicates that OsWRKY19 can activate the expression of OsPR10. To verify whether OsWRKY19 can directly activate the expression of OsPR10, we analysed the promoter region of OsPR10 using the PlantCARE database and detected four potential elements: three W-boxes and one WLE 1 (Figure  7c ).
Acknowledgements The authors are grateful to  Prof. Yuxuan Hou  and  Song Liu  from the  China Rice Research Institute, Hangzhou Province , for kindly providing the Xoo strain. We thank  Tongming Wang  for his help in manuscript revision. This work was supported by grants from the  National Key Transform Program  ( 2016ZX08001-002 ),  National Key Program for Research and Development  ( 2017YFD0100201 ,  2017YFD0100202 ) and the  Project of ChongQing Science & Technology Commission  ( cstc2018jscx-msybX0250 ).
, 2007; Gallardo et al. , 2014; Mérigoux et al. , 2009)  and (ii) the taxonomic richness of aquatic gastropoda known to increase with disconnection and late successional stages in floodplain waterbodies  (Jurkiewicz-Karnkowska and Karnkowski, 2013; Reckendorfer et al. , 2006). Time series for the two invertebrate metrics were calculated from species occurrence data predicted by BRT that used upstream overflow and backflow time series as inputs. We expected EPT and gastropod richness to respond differentially to changes in LHC  (Gallardo et al. , 2014; Paillex et al. , 2007). For the ecological reasons given above, EPT richness is expected to increase with upstream overflow connection  (Paillex et al. , 2009). On the contrary, gastropod richness is expected to increase with upstream disconnection and the dominance of backflow  (Obolewski et al.
Sites Data were collected between 2006 and 2016 in two adjacent reaches of the French upper Rhône River (sectors of Belley [BELL] and Brégnier-Cordon [BREG], Fig. 2 ) as part of the long-term monitoring of the river restoration  (Lamouroux and Olivier, 2015). Nineteen floodplain channels were sampled, covering the full array of lateral hydrological connectivity, from channels completely and permanently connected at both ends to the main river, to channels fully disconnected at average water level. Some of these channels were restored through dredging and/or reconnection to the river in 2005 (BELL) and 2006 (BREG; Appendix A).
Macroinvertebrate data In most floodplain channels, zoobenthic assemblages were sampled in two sites (upstream and downstream portions of the channel). In two channels, sampling was limited to one site, and extended to three in four others (Appendix A). In each site, three samples were randomly distributed along a 30 m stretch. In lentic conditions, macroinvertebrates were sampled within a 0. 5 × 0. 5 m metal frame with a hand net (mesh size 500 μ). Aquatic macrophytes and the sediment surface (i. e. litter) were covered. In lotic conditions, the same surface area was sampled with the same net in a kick-sampling approach to cover the mineral substrate, as well as debris and macrophytes when present. The material collected was fixed in ethanol and sorted in the laboratory. Benthic invertebrates were identified to species or genus when possible.
A survey consisted of local measures (25 and 19 points for MOLO and PONT, respectively) of water depth and fine sediment thickness [see Section 2. 2 of  Riquier et al. , 2017  for further details]. The evolution of sediment thickness in PONT and MOLO was estimated following a power equation [the most realistic for this type of floodplain channel; see Section 4. 3 of  Riquier et al. , 2017]  of the two models (Eq. (  1 ), Appendix B). We used local values of fine sediment thickness and initial water depths instead of channel-averaged ones to account for longitudinal variability of water depth within the two channels. For the first approach (i), regression coefficients were estimated individually for each local measure of fine sediment thickness (individual point-based models) using the nlminb function of the Stats package (R Development Core Team, 2016).
Regarding (ii) the NLME model, which allows inclusion of fixed and random effects in the regression, we considered the β parameter as a fixed parameter shared by all local measures performed in MOLO and PONT channels (i. e. reflecting a constant decay rate shared by all modelled local station), whereas the α parameter was allow to vary across local measures. This model was fitted by maximum likelihood with the nlme Table  1  Main geomorphological and hydrological features of the two channels used as starting points in the succession scenario 2 [adapted from  Riquier et al. , 2017]. For upstream connection duration, averages are associated with the min-max range in brackets. function of the package nlme (R Development Core  Team, 2016). The main parameters used to estimate the potential persistence of floodplain channels as aquatic habitats and their range of values are summarized in Table  2.
thresh function (SDMTools version 1. 1 in the R package;  VanDerWal et al. , 2014). Predicted species occurrences were assembled into a single dataset for calculation of the two diversity metrics (gastropod and EPT richness). The quality of the prediction was assessed by the area under the curve (AUC)  (Powers and Ailab, 2011)  and the contribution of the variables using their relative influence  (Friedman, 2001). Statistical analyses were conducted in the R statistical environment (version 1. 1. 423, R Development Core Team, 2016). We fitted GBRT using the gbm package (version 2. 1. 4, Ridgeway, 2006). We used the ggplot2 package  (Wickham, 2009)  for all graphics. A nonparametric loess regression was displayed using the loess function (span = 0. 1 for scenario trends and 0. 5 for other curves) in the ggplot2 package  (Wickham, 2009).
Calculation and validation of the predicted diversity metrics To ensure the representativeness of predictions  (Barnard et al. , 2019; Zhang and Li, 2018) , the full dataset was randomly split into calibration and testing sets (70% and 30% of the data, respectively) and each split was iterated 50 times (i. e. 50-folds cross validation)  (Barnard et al. , 2019; Scherrer et al. , 2018; Zhang and Li, 2018). For each iteration, we quantified the accuracy of the model predictions compared to the testing set at two levels: (i) for the taxa models with the calculation of the cross-validated area under-the-curve (Appendix C); and (ii) for the taxonomical richness by computing the slope, the intercept and the R-squared of the linear regression model of predicted versus observed values contained in the testing set.
Following  Jusup et al. (2009) , the linear Hypothesis function from the car package  (Fox and Weisberg, 2019)  in R was used to compute Wald-testbased comparisons between a given model i. e. the linear relation of observed vs. predicted values, and the restricted equality model y = x. In each scenario, because predictions were produced at each iteration, the random split procedure reported 50 different datasets that were subsequently averaged to provide a final occurrence prediction for each taxa. In order to reduce overestimation of species richness  (Guisan and Rahbek, 2011; Jiménez-Valverde and Lobo, 2007) , richness metrics were calculated for the predicted assemblages (i. e. the assemblage of predicted EPT and gastropod taxa) by summing the averaged predicted occurrences of taxa of the final dataset.
g. Aplexa hypnorum, Anisus spirorbis, Planorbis planorbis) were missing from the models because of their very low occurrence or absence from the Rhône floodplain dataset. When accounted for, such species could reduce the decrease rate of gastropod richness at late successional stages  (Bogan et al. , 2013; Jurkiewicz-Karnkowska and Karnkowski, 2013; Nicolet et al. , 2004). For these reasons, the proposed models can be of interest to inform decisions related to the type of reconnection to select for a floodplain channel (e. g. full vs. downstream-only), given local geomorphological, hydrological or technical constraints in a restoration project. The sedimentation rate in restored channels is a crucial issue bearing consequences upon the life duration of these habitats  (Riquier et al. , 2017)  and the potential need to plan for recurring restoration operation.
Acknowledgements This study was funded by the  Agence de l'Eau Rhône-Méditerranée-Corse , the  Compagnie Nationale du Rhône , the  Région Auvergne-Rhône-Alpes , the  Région Provence-Alpes-Côte d'Azur and Electricité de France. The work was performed in the context of the  OHM Vallée du Rhône , the  LabEx DRIIHM  and in the  EUR H2O ' Lyon  ( ANR-17-EURE-0018 ) of  Université de Lyon. We thank all the research teams involved in the monitoring of the Rhône River ecological restoration ( RhônEco programme ) for making data available and for discussions. We thank the  Syndicat du Haut-Rhône  for their technical support. We are grateful for the helpful suggestions and comments by two anonymous reviewers.
, 2008; Van As and van Duynhoven, 2013) , X-ray computed tomography (CT)  (Mooney et al. , 2012; Metzner et al. , 2015) , or neutron tomography  (Matsushima et al. , 2008; Moradi et al. , 2011). Image-based methods (e. g. , relying on the use of scanners or cameras) are mostly used for measuring the size, architecture, and other structural shoot and root traits. They allow hundreds of plants to be daily phenotyped, given the short time required for image acquisition  (Clark et al. , 2013; Adu et al. , 2014; Le Marié et al. , 2014; Slovak et al. , 2014). Several software packages have been developed for root imaging and extracting quantitative data from captured images. ImageJ is an open source Javabased image analysis program, which is customizable with a variety of macros and plugins available, some of them written specifically for plant phenotyping applications.
This program has been used, for example, in the IJ-Rhizo  (Pierret et al. , 2013)  and SmartRoot software  (Lobet et al. , 2011). The Plant Image Analysis web site 1   (Lobet et al. , 2013)  provides an on-line database and imaging solutions, commercial as well as open sources, for analyzing biology of plants. It provides a complete overview of existing software for root image analysis. Some of these tools require manual inputs from the user such as selecting points or tracing lines on the root, while others are automatic or semiautomatic. The most popular methods in root image processing are summarized in Table  1 , according to the root characteristics requested in our study. Identification of roots (adventitious and/or lateral) as distinct objects is an important goal for quantifying plant responses to various abiotic stresses including water stress and nutrient deficiency.
Exposure time used for image acquisition was 80 s and ten images (with this exposure time) of open beam were collected for data normalization. Every collected image was initially analyzed by Image J 1. 48  (Abràmoff et al. , 2004). We firstly cropped the image to select the object of interest (sample holder with the roots) and reduced the size of the data to analyze. We also used this software in order to remove noise (despeckle command) and white points due to gamma rays (remove outliers command, radius of 2 px; threshold of 50), to normalize the images by the open beam, and to correct the eventual tilt of the object of interest. Exported images were saved in. tiff format. Octopus 8. 7 (Octopus Imaging, Inside Matters, Belgium) was used to reconstruct the 3D image. This software allowed a last filtering step to remove rings due to scattering phenomena and building sinograms.
Then, the reconstruction was realized, recording the output images in 32 bit. At the end of this step, the intensity values were set up between 0 and 1. After reconstruction with Octopus, a stack of 1074 slides (from the top to the bottom of the sample holder) was cropped and rescaled (50%) on Image J. The Avizo Fire 9. 2 (FEI, Hillsboro, Oregon, United States) software was then used to visualize the 3D volume. The quantification of the pixels corresponding to the roots on entire stack of reconstructed slides has been performed with the toolbox particle analysis of the Image J 1. 48 software.
It is therefore no longer an average diameter as with WinRhizo, SmartRoot or more recently RootGraph software. This local diameter makes it possible to calculate even more parameters such as the apical diameter of the roots and the unbranched apical zone of the primary root, etc. , All the phenotyping traits, which are determined with the previous procedure, are presented in the Table  1 , even if some of them present no biological significance for vine as they deal with nodules. Compared to semi-automatic or manual methods/ algorithms (Table  1 ), the main advantages of our method concern the calculation time (4 min processing vs. 3 h for SmartRoot for example), the number of traits which can be determined and the automatic procedure.