dataesr/scientific-paragraphs-categorization huggingface dataset:
2 eV after a ptychography carbon K-edge stack using a defocused (1. 0 μm) spot size (estimated total dose of 62 MGy). The advantage of using a defocused rather than a focused spot is abundantly clear. Phase imaging and phase spectroscopy of CNT. Figure  5  presents results from ptychographic phase images of the CNT sample. Figure  5a  is the average (after alignment) of all 24 ptychographic phase images in a stack measured from 282 to 296 eV, with 0. 5 eV steps. LV polarization was used. The average of the 24 images is presented, in part to optimize statistics, and in part to show the stability of the STXM and the scanning system. Clearly, ptychography data sets recorded with this scanning platform retain high spatial resolution information across images recorded at multiple photon energies.
Rectilinear raster scanning was used. The chamber was maintained at a pressure of 4 × 10 -6 mbar throughout the experiment. Ptychographic reconstruction was carried out using the open-source PyNX software  50  developed at the European Synchrotron Radiation Facility. The Alternate Projection  51  algorithm with a single probe function was used. The number of iterations varied between 600 and 1000 (see Supplementary Table  1  for details). Although the reconstruction computer is equipped with two graphical processing units, only one was used in these reconstructions. A typical, single image reconstruction took ~20 min. Recently PyNX has been improved to use multiple graphical processing units which will significantly reduce ptychography reconstruction times. When the data were acquired, the ethernet network at the HERMES beamline had an upper transfer rate of 1 Gb/s.
Since the typical ptychographic dataset size is >10 Gb, data transfer from the acquisition & control computer to the PyNX workstation was a bottleneck. Recently the beamline was upgraded to 10 Gb/s transfer rate. Samples. The calibration sample with both the Siemens Star and line-space patterns was fabricated at the Paul Scherrer Institute (Villigen, Switzerland) by the X-ray optics and applications group headed by Christian David. The carbon nanotube sample was synthesized by the Simard group (National Research Council, Ottawa, Canada) using a two-stage laser pyrolysis method  39. It was the subject of an extensive study of its chemical functionalization using STXM, Raman, thermal analysis, and other methods  38. The specific sample used for this study was subjected to air oxidation for about 10 years and thus exhibits much less dichroism than when studied originally  38.
Acknowledgements The research was carried out at the  HERMES beamline of Synchrotron SOLEIL. We acknowledge SOLEIL  for providing the beam time needed. We thank the staff of SOLEIL for their support, in particular  Arafat Noureddine  for his help with the camera control. We especially thank  A. Besson  and  J. -B. Boy  for their technical support,  E. Fahri  and  F. -E. Picca  for the computing support, and  K. Medjoubi  for his help with the reconstruction and fruitful discussions. We thank  Benoit Simard  and  Benedikt Rosner  for supplying the samples. This work was partially funded by project  EU-H2020  of the  Nanoscience Foundries and Fine Analysis (NFFA) program. Participation of the Canadian group was supported by the  Natural Science and Engineering Research Council of Canada  and funding from the  Faculty of Science of McMaster University.
Data availability The processed data (spectra, images, analysis of images) are available from the authors upon request. The diffraction images and other measured data, along with associated metadata, are saved in the central repository of Synchrotron Soleil, as per the European FP7 policy, and can be accessed as per Soleil protocols. [https://www. synchrotronsoleil. fr/en/file/11308/download?token=9mSpYtJC].
1 1 Fig. 2 Ptychographic imaging of the carbon nanotube (CNT) sample. a Diffraction image from a defocused, 1. 0 μm spot of 285. 2 eV soft X-rays hitting several carbon nanotubes. The false-color scale is log 10 of the X-ray counts averaged over 30 images, each recorded with 500 ms exposure time. The corners of the image correspond to a momentum of 0. 0160 nm -1. b Amplitude image of the CNT sample from the reconstruction of a ptychographic dataset measured at 285. 2 eV in a region similar to that where (a) was measured. The yellow annulus indicates the 1. 0 μm diameter annular probe used for defocus ptychography imaging. The scale bar is amplitude in arbitrary units (see Supplementary Table1for acquisition details). c STXM image of the same area acquired with the 62 nm FZP, just before the ptychography measurement.
More specifically, leaf physiological, chemical, and morphological trait variations have long been the focus of many studies in the areas of crop biology and viticulture  [31]. However, to the best of our knowledge there have been no studies explicitly evaluating whether wine grapes vary along an infraspecific LES, or if the shape of an infraspecific wine grape LES differs from that Plants 2022, 11, 2792 3 of 17 observed among plants globally. This study aimed to fill this gap by quantifying nine LES and related leaf traits in five widely cultivated wine grape varieties ('Chardonnay', 'Pinot Gris', 'Cabernet Sauvignon', 'Merlot', and 'Syrah') at two growth stages (post-flowering and veraison). These data were then used to (1) quantify differences in LES traits across wine grape varieties and at different growth stages; (2) determine whether an infraspecific LES in wine grapes exists; and (3) test whether wine grapes differ from wild plants in their LES trait relationships.
3 and 31. 6 • C. All leaves were allowed to stabilize at these conditions for at least 5 min, prior to logging data, and values for these traits were calculated as the mean of three Once physiological measurements were completed, leaves were collected and transported to the University of Toronto Scarborough, Canada, for analyses of morphological and chemical traits. Here, leaves were first weighed for fresh leaf mass (g), and then an LI-3100C leaf area meter (LI-COR Biosciences, Lincoln, NE, USA) was used to measure leaf area (cm 2 ). Subsequently, all leaves were dried at 60 • C to constant mass and re-weighed for dry mass (g). These data were used to calculate LMA (g m -2 ) as dry mass/fresh area, and LMA data were, in turn, used to derive mass-based maximum photosynthetic rates (A mass , mmol CO 2 g -1 s -1 ) as A max /LMA.
Data Analysis-Causes of Intraspecific Trait Variation in Wine Grape Varieties All statistical analyses were performed using R v. 4. 1. 0 (R Foundations for Statistical Computing). The first analysis evaluated statistical distributions for all traits using the 'fitdist' function in the 'fitdistrplus' R package  [35] , to identify which traits were normally or log-normally distributed, as inferred by the highest log-likelihood values. Based on these results, descriptive statistics for each trait across our entire dataset (n = 90 observations for each trait) were calculated, which included means and standard deviations (SDs) for normally distributed traits, and medians and median absolute deviations (MADs) for log-normally distributed traits. Coefficients of variation (CVs) were also calculated for all traits.
An analysis of variance (ANOVA) procedure, coupled with Tukey's honestly significant difference (HSD) post hoc tests, was then used to evaluate whether traits varied as a function of growth stage (i. e. , post-flowering or veraison), variety identity, and planting row, as well as all two-and three-way interactions. This procedure was then paired with a variance partitioning analysis, employed in previous analyses of intraspecific trait variation  [15, 36] , to identify the factors that explained the highest proportion of variability in grape traits. This entailed first fitting a linear mixed effects model with nested random effects using the 'lme' function in the 'nlme' R package  [37]. In this model, nested random effects were parameterized as planting rows nested within varieties which were nested within the growth stage; a random intercept was included as the only fixed effects  [36].
Data Analysis-Bivariate and Multivariate Trait Correlations Pearson correlation tests were used to evaluate all pairwise trait relationships across the entire dataset (n = 90 observations total for each test). Multivariate relationships among grape traits were then examined using a principal components analysis (PCA), which was implemented with the 'rda' in the 'vegan' R package  [39]. In this PCA, all trait data were scaled to unit variance, and A max was excluded due to its strong correlation with A mass (r = 0. 801, p < 0. 001). The 'dimdesc' function in the 'FactoMineR' R package  [40]  was then used to evaluate the statistical relationships between individual traits and the first two principal component axes. Multivariate analysis also included a permutational multivariate analysis of variance (PerMANOVA), which was designed to test whether multivariate trait syndromes varied significantly as a function of planting row, variety, and growth stage, as well as all two-and three-way interactions among these factors.
This PerMANOVA was performed using the 'adonis' function in the 'vegan' R package  [39]  and was based on n = 999 permutations. Finally, we used ANOVA coupled with a Tukey HSD post hoc Plants 2022, 11, 2792 5 of 17 test (implemented as above) to test whether PCA axis 1 and 2 scores varied as a function of growth stage, variety identity, and planting row, and all two-and three-way interactions.
Data Analysis-An Intraspecific LES across Wine Grape Varieties The final statistical analysis evaluated relationships among three leaf traits which are central in the LES hypothesis, including LMA, A mass , and leaf N  [2]. Here, standardized major axis (SMA) regression analysis was used to quantify pairwise trait relationships in grapes, and compare their shape (i. e. , SMA slopes) and strength (i. e. , SMA r 2 values) with those same trait relationships observed among plants globally. This analysis entailed first fitting an SMA regression to the grape trait dataset (n = 90 leaves total) using the 'sma' function in the 'smatr' R package  [41] , and then performing this same analysis on plant species in the GLOPNET dataset of Wright et al. [2]. These GLOPNET analyses were based on n = 764 species with paired LMA-A mass data, n = 1958 species with paired LMA-leaf N data, and n = 706 plant species with paired A mass -leaf N data.
Statistically significant effects (where p < 0. 05) are highlighted in bold, and abbreviations are as follows: A max : light saturated maximum photosynthetic rate on a per-unit leaf area basis; A mass : light saturated maximum photosynthetic rate on a per-unit leaf mass basis; g s : stomatal conductance; WUE: instantaneous water use efficiency; LMA: leaf mass per unit area; PCA 1: principal component 1 score; PCA 2: principal component 2 score. Results and associated post hoc tests for individual traits are presented in Figure  1. With the exceptions of WUE and g s , variance partitioning and ANOVA indicated that variety identity, growth stage, and a variety-by-growth stage interaction term were the most important factors determining leaf trait variation in our dataset (Table  2 ). Across all traits except for WUE and g s , the combination of variety identity and growth stage explained between 27.
3% and 81. 5% of trait variation (Figure  2 , Table  1 ). Moreover, except in the case of A max , traits varied significantly as a function of variety and growth stage (Figures  1  and 2 , Table  1 ). Across varieties, 'Pinot Gris' most consistently expressed a suite of traits that were the clearest 'resource-acquiring' syndrome. In the dataset, 'Pinot Gris' expressed among the highest values of A max (15. 1 and 13. 7 µmol CO 2 m -2 s -1 in the post-flowering and veraison stages, respectively), A mass (0. 256 and 0. 186 µmol CO 2 g -1 s -1 in the post-flowering and veraison stages, respectively), and leaf N (3. 4% and 2. 5% in the post-flowering and veraison stages, respectively), and the lowest LMA values (59. 9 and 73. 4 g m -2 in the post-flowering and veraison stages, respectively; Figure  1 ).
A Leaf Economics Spectrum across Wine Grape Varieties Relationships among three core LES traits evaluated here including A mass , leaf N, and LMA, closely matched patterns of LES trait variation observed among plants globally. This included positive SMA relationships among A mass and leaf N (SMA model r 2 = 0. 189, p < 0. 001), and negative relationships between LMA and A mass (SMA model r 2 = 0. 191, p < 0. 001), and LMA and leaf N (SMA model r 2 = 0. 507, p < 0. 001; Figure  5 , Table  S4 ). Positive scaling relationships between A mass and leaf N in grape (SMA model slope = 0. 12) were statistically indistinguishable from the A mass -leaf N observed in the GLOPNET dataset of plants globally (SMA model slope = 0. 11, slope test r = 0. 03, p = 0. 77; Figure  5 , Table  S4 ). Analysis did identify statistically significant differences in LES trait scaling relationships between LMA and leaf N in grapes which were steeper (SMA model slope = -0.
04) vs. the GLOPNET dataset (SMA model slope = -0. 008; slope test r = 0. 96, p < 0. 001; Figure  5 , Table  S4 ). Similarly, relationships between A mass and LMA in grapes (SMA model slope = -225. 2) differed statistically from those observed in the GLOPNET dataset (SMA model slope = -868. 1; slope test r = -868. 1, p < 0. 001; Figure  5 , Table  S4 ). Plants 2022, 11, x FOR PEER REVIEW 11 of 17.
A Leaf Economics Spectrum across Wine Grape Varieties Relationships among three core LES traits evaluated here including Amass, leaf N, and LMA, closely matched patterns of LES trait variation observed among plants globally. This included positive SMA relationships among Amass and leaf N (SMA model r 2 = 0. 189, p < 0. 001), and negative relationships between LMA and Amass (SMA model r 2 = 0. 191, p < 0. 001), and LMA and leaf N (SMA model r 2 = 0. 507, p < 0. 001; Figure  5 , Table  S4 ). Positive scaling relationships between Amass and leaf N in grape (SMA model slope = 0. 12) were statistically indistinguishable from the Amass-leaf N observed in the GLOPNET dataset of plants globally (SMA model slope = 0. 11, slope test r = 0. 03, p = 0. 77; Figure  5 , Table  S4 ). Analysis did identify statistically significant differences in LES trait scaling relationships between LMA and leaf N in grapes which were steeper (SMA model slope = -0.
04) vs. the GLOPNET dataset (SMA model slope = -0. 008; slope test r = 0. 96, p < 0. 001; Figure  5 , Table  S4 ). Similarly, relationships between Amass and LMA in grapes (SMA model slope = -225. 2) differed statistically from those observed in the GLOPNET dataset (SMA model slope = -868. 1; slope test r = -868. 1, p < 0. 001; Figure  5 , Table  S4 ). S4. Trait abbreviations are as follows: A mass : light saturated maximum photosynthetic rate on a per-unit leaf mass basis; LMA: leaf mass per unit area.
5% and 3. 3%, and LMA ranging from ~30 to 270 g m -2  [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54]. The dataset in this study broadly aligns with these findings, indicating that unlike other crops such as soy [13], wheat, and maize  [17] , which occupy the extreme "resource-acquiring" end of the LES trait space, wine grapes are largely intermediary in their LES trait values when compared with plants globally (Figure  5 ). The dataset here provides some evidence indicating that certain varieties ('Chardonnay' and 'Pinot Gris') express more resourceacquiring trait syndromes than others ('Cabernet Sauvignon', 'Syrah', and 'Merlot'). This trend is intriguing and points to differences in red vs. white varieties; however, considerable overlap remains among varieties within the larger wine grape trait space (Figure  5 ), and our dataset contains too few varieties to support robust analyses of differences in trait syndromes across reds vs.
This also aligns with recent studies showing that within 'Chardonnay' alone, vines differ in their resource capture traits in response to soil compaction  [61]. Here, this study found that in wine grape varieties, relationships between leaf N and A mass are statistically indistinguishable from the same relationship observed in plants globally (Figure  5 ). Additionally, although other LES trait relationships in grape evaluated here including those between LMA, A mass , and leaf N, did differ statistically from those quantified in the GLOPNET dataset, grape LES traits and their relationships were broadly aligned in both datasets (Figure  5 ). This is unlike certain crops, including rice  [19]  and coffee  [15]  which, due to their domestication syndromes that favour greater resource-use efficiencies or concentrations of secondary compounds, exhibit LES trait relationships that differ statistically from those observed in wild plants.
Instead, wine grapes appear to align more closely with crops such as soy whose LES relationships match closely with wild plants  [13]. When taken with the broader literature on crop traits, the following hypothesis emerges: LES trait relationships in crops differ from those in wild plants, when the domestication syndrome entails targeted alterations to the N economy of leaves and plants. In the present study, the sample sizes for individual varieties (n = 18) were too small to evaluate whether varieties differed in their LES trait relationships  [62]. However, one emerging question within studies on intraspecific leaf trait relationships, is whether or not plants are constrained along a single LES which is unique to a given species or genotype. To date, this has been only weakly addressed, with different datasets from coffee indicating that a single intraspecific LES describes plant trait syndrome differences across growing conditions  [15]  and ontogeny  [14].
Additionally, data and SMA models for the same LES trait relationships observed among wild plants in the GLOPNET dataset are shown in all panels (grey dashed trend lines and points). SMA models were fit to the 'Wine grape variety' dataset, and wild plants were derived from the GLOPNET dataset (Wright et al. , 2004). Full model diagnostics for each SMA model in (Panels A-C) are presented in TableS4. Trait abbreviations are as follows: A mass : light saturated maximum photosynthetic rate on a per-unit leaf mass basis; LMA: leaf mass per unit area.
Transcriptome data analysis Adapter removal and quality trimming of reads were done using Adap-terRemoval v. 2. 1. 7 (parameters: -min quality 20, -min length 50)  44. RNA-seq reads were mapped to the Arabidopsis reference transcriptome TAIR10 v. 47, with ribosomal RNA regions (2:3471-9557; 3:14197350-14203988) masked, using TopHat 2. 0. 13 (no-mixed alignments; up to 20 secondary alignments; no new junctions)  45. Read counts covering transcripts were computed using the featureCounts program in R  46  , and submitted to differential gene expression analysis in DESeq2 (v. 1. 32. 0) in R (v. 4. 1. 0) (default parameters; significance conditions: base expression > 5, false discovery rate (FDR) adjusted P value < 0. 05, |log 2 FC| > 1)  47.
Gene ontology term analysis The online tool AmiGO 2  48  (PANTHER overrepresentation test released 20210224; GO Ontology database released 2 July 2021) was used to identify GO terms in biological processes that are over or under-represented in XopD-dependent DEGs. Binomial tests were performed and FDR adjusted P value < 0. 05 was considered as significant. Gene-concept network plot was conducted by the cnetplot function in the cluster-Profiler package (clusterProfiler v. 3. 18. 1)  49  in R (v. 4. 0. 5).
Determination of in planta bacterial populations Whole leaves or symptomatic leaf areas infected with a given Xcc strain were collected at seven DPI and placed between clear plastic foils. The leaf sizes (cm 2 ) were determined on scanned images using ImageJ software (v. 2. 0. 0-rc-69/1. 52p). Each leaf sample was then homogenized in 400 μl of sterile water in safe-lock tubes containing a 5 mm ceramic ball using the TissueLyser II (QIAGEN). Serial dilutions of the homogenates were performed, and a 4 μl drop of each dilution was spotted three times on nutrient yeast glycerol plates supplemented with appropriate antibiotics. The plates were incubated at 28 °C for 48 h, and colonies were counted in spots containing 3 to 30 colonies. The mean of bacterial populations per cm 2 of leaf tissue was calculated with three technical replications per sample.
Acknowledgements We thank  Z. -M. Pei  and  F. Yuan  for contributing osca1-1, ProOSCA1. 1:OSCA1. 1-GFPand Pro35S: OSCA1. 1/osca1-1Arabidopsis seeds;  D. Weigel  for contributing pFK-386 and pFK-209 plasmids;  J. -Y. Yang  for contributing ProXVE: XopD Arabidopsis  seeds; and  A. G. Andrade-Galan  for technical assistance. Our study benefits from the infrastructure at the ZMBP and the  Poznań Supercomputing and Networking Center ; the LIPME as a part of the  French Laboratory of Excellence project  ( TULIP ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ) and the  COST action CA16107 EuroXanth  to L. D. N. ; and the  Deutsche Forschungsgemeinschaft (DFG ,  SFB 1101  project  D08  and  LA 1338/9-1 ) to T. L. This work was supported by the  Institutional Strategy Program of the University of Tübingen  ( DFG, ZUK 63 ) and the  Deutsche Forschungsgemeinschaft (DFG , No.
Data availability RNA-seq and EM-seq data have been deposited with ArrayExpress database (https://www. ebi. ac. uk/biostudies/arrayexpress) accession numbers E-MTAB-10280 and E-MTAB-10281. The Arabidopsis reference genome (TAIR10 v. 47) and gene annotation are publicly available at http://ftp. ensemblgenomes. org/pub/plants/release-47/. Araport11 annotation for transposons and pseudogenes are publicly available at https://datacommons. cyverse. org/browse/iplant/home/araport/pub-lic_data/Araport11_Release_201606/annotation. The authors declare that all other data supporting the findings of this study are available in the main text or the supplementary materials. Source data are provided with this paper.
Deployed on 24 selected headwater catchments from 12 to 141 km 2 located in north-western France, the method successfully matches the stream network in 80% of the cases and provides catchment-scale hydraulic conductivities between 9 x 10 -6 and 9 x 10 -5 m s -1 for shallow aquifers sedimentary and crystalline rocks. Results show a high sensitivity of  to the density and extension of the low-order streams and limited impacts of the DEM resolution as long the DEM remains consistent with the stream network observations. With the emergence of global remote-sensing databases combining information of highresolution DEM and stream network, this approach will contribute to assess hydraulic properties of in shallow headwater aquifers.
Topography and model geometry We first select the digital elevation model (DEM) that will be defined as the upper boundary of the groundwater model. In this study, we use the 75 m grid resolution DEM available at the scale of France. It is generated from photogrammetric restitution and provided by BD ALTI  (IGN, 2021). We also explore the impact of different DEM resolutions on the final estimations of /. We consider two higher resolution DEMs of 5 m and 25 m also provided by BD ALTI. For coarser resolutions, the 25 m DEM was downsampled with nearest neighbour option to larger cell sizes, i. e. 100 m, 200 m, and 300 m. Geospatial processing is performed using the software WhiteBoxTools available in Python  (Lindsay, 2016) , labelled WBT in the following. All functions of WBT used are quoted in brackets in the following.