dataesr/scientific-paragraphs-categorization huggingface dataset:
We thus assume that it has no impact given the erosion of the polygons within our LAI retrieval method (Sect. 2. 2. 1). The uncertainties with radiometry are also rather small compared to the measurements' uncertainties while the signal-to-noise ratio (SNR) is satisfying. The inner-field variability (green area, Fig. 3 ) is also clearly smaller than the measurements' uncertainties. However, some differences between the measured and remotely sensed LAI can be found. Particularly, the maximum LAI in Formosat-2 data tends to be lower than the measured one. This is due to a saturation effect of the remotely sensed LAI, pointed out by  Veloso et al. (2012). But the impact on the simulated evapotranspiration amplitude is not significant, as shown by the example of a winter wheat field (Figs. 3a  and 4a ).
Including irrigation will improve the simulations  (Garrigues et al. , 2015). An automated irrigation module might therefore be a significant improvement even if it often relies on poorly known soil parameters, like the available water content for evapotranspiration depending on the field capacity, the wilting point, and the root depth. Incorporating irrigation rules similar to those of the MAELIA platform  (Therond et al. , 2014)  could add up to the differences observed in this study and give even better results. Adding a new set of spatialized soil parameters, to estimate more efficiently the maximum available water content of the soil, could be of great interest. These issues will be addressed in the future. The spatialized comparison also shows that the soil water content, the drainage, and the runoff are significantly increased (Fig.
Conclusion This study deals with the inter-comparison between two spatialized hydrometeorological ISBA modeling approaches in an agricultural zone in southwestern France from year 2006 to 2010. A first experiment was performed with LAI forcing from ECOCLIMAP-II database which was generated from MODIS data. It was considered as the reference simulation. The second experiment included LAI forcing from high-resolution Formosat-2 (Sentinel-2 like) time series data. Both simulations were performed with plots as computing units, where plot segmentation was derived from Formosat-2 high-resolution land cover maps classifications. The use of the plot-scale approach allowed for exploiting the high spatial resolution on coherent hydrometeorological units while limiting the calculation time compared to a pixel-based approach.
Acknowledgements. This study was conducted as part of the  REGARD  project  entitled Modélisation des REssources en eau sur le basin de la GAronne , interaction entre les composantes naturelles et anthropiques et apport de la téléDétection (http://www. cnrm-game-meteo. fr/spip. php?article809) funded by the  French Sciences and Technologies for Aeronautics and Space Foundation  (http://www. fondationstae. net). We thank  Mathieu Coustau  for his work, which formed the basis of our paper. Our sincere thanks go to  Claire Marais-Sicre , who provided the Formosat-2 land cover maps. We thank all the people of the OSR who contributed to the collect and the process of data. OSR facilities and staff are funded and supported by the  Observatory Midi-Pyrenean , the  University Paul Sabatier, Toulouse, France ,  CNRS  ( Centre National de la Recherche Scientifique ),  CNES  ( Centre National d'Etude Spatial ), and  IRD (Institut de Recherche pour le Développement).
Code and data availability. The version of SURFEX used in this study is the v7. 3. The code is available at http://www. umr-cnrm. fr/ surfex//data/BROWSER/out_doc73/index. html. For the LAI data or land cover maps, please contact the corresponding author. The Supplement related to this article is available online at https://doi. org/10. 5194/hess-21-5693-2017supplement.
Oscillation Frequency vs. Cell Size Depending on cell size, the tissue characteristic frequency D 0 /R 2 can remain out of reach using OGSE and conventional diffusion gradients, thus preventing a good sampling of the diffusion timedependence. Figure  3A  highlights the diffusion behavior over a realistic range of cellular size radii (R = 1-10 µm) and ICS free diffusivity (D 0 = 2 µm 2 /ms). Without a dedicated gradient insert, the only oscillation frequencies that can be probed with sufficient (C) Diffusivity inside impermeable spheres (black) and its frequency-derivative: the instantaneous dispersion rate (gray, arbitrary units). Oscillations frequencies are normalized to the tissue characteristic frequency D 0 /R 2. The EM was extracted from the dataset used for cell size measurement in Reynaud et al.
The POMACE model The POMACE (Pulsed and oscillating gradient MRI for assessment of cell size and extracellular space) model combines multiple OGSE and PGSE measurements in different time domains (Figure  4B ). Microstructural parameter estimation is performed in two steps. The surface-to-volume ratio and free diffusivity are first evaluated using high-frequency OGSE in the short-time regime  [18]  using Equation  (2). These values are then used as constraints when fitting the lowfrequency OGSE and PGSE data (Figure  5 , f OGSE < 88 Hz) to a model of impermeable spheres bathing in ECS  [15]. Using a dedicated histology coil  [47] , the validity of POMACE was tested ex vivo. ICS maps correlated well with optical microscopy performed on the same samples used for MRI  [15]. In vivo, ICS estimates were found in agreement with ECS estimates from an effective medium theory  [25] , while cell sizes matched electron microscopy measurements in mice gliomas (GL261).
Similarly, synthetic diffusion data was generated using the IMPULSED framework  [46]  in order to mimic TDD in colorectal tumors (see Table  1 ). Multiple instances of gaussian noise (typical in vivo SNR = 120, n = 2,500) were added to the signal before fitting. Although the distribution of fit estimates were not found normal (Figures  6C, D ), average fit estimates matched the ground truth (SNR = ∞) with good accuracy for most parameters (Table  1 , relative bias below 3/13% for f, R, D ics for DiFi/HCT116 cell lines). The matlab code used to generate synthetic tumor data and plot parameter accuracy with POMACE and IMPULSED is readily available for download at https://github. com/oreynaud/FIT_TDD.
Fixing Parameters Because of model over-parametrization, most TDD frameworks resort to fixing one or several parameters in order to improve the   [15]. Fit estimates distribution when characterizing tumor microstructure in vivo inside DiFi (C) and HCT116 (D) colorectal tumors with IMPULSED  [46]. The ground truth is indicated by a black square. For each framework, the full list of fit estimates can be found in Table  1. The matlab code used to generate synthetic tumor data and plot parameter accuracy with POMACE and IMPULSED is readily available for download at https://github. com/oreynaud/FIT_TDD. fit stability and precision. This comes at the expense of accuracy, because errors on fixed parameters can propagate into the remaining fit estimates. In the first VERDICT framework, the ICS and ECS diffusivities were fixed based on fit optimization performed on preliminary data  [16] , and found consistent with values derived from ex vivo studies with high SNR  [57].
ACKNOWLEDGMENTS This work was supported by  Centre d'Imagerie BioMédicale (CIBM)  of the  UNIL ,  UNIGE ,  HUG ,  CHUV ,  EPFL  and the Leenaards and  Jeantet Foundations. The author would also like to thank  Dr. Sungheon G. Kim ,  Dmitry S. Novikov , and  Ileana O. Jelescu  for stimulating discussions on the matter.
Antimicrobial resistance (AMR) in bacteria and associated human morbidity and mortality is increasing. Use of antimicrobials in livestock selects for AMR that can subsequently be transferred to humans. This flow of AMR between reservoirs demands surveillance in livestock as well as in humans. As part of the EFFORT project (www. effort-against-amr. eu), we have quantified and characterized the acquired resistance gene pools (resistomes) of 181 pig and 178 poultry farms from nine European countries, generating more than 5,000 gigabases of DNA sequence, using shotgun metagenomics. We quantified acquired AMR using the ResFinder database and a database constructed for this study, consisting of AMR genes identified through screening environmental DNA. The pig and poultry resistomes were very different in abundance and composition.
Sampling to estimate the effect of random sampling To study the potential effect of sampling randomness and the reproducibility of our sampling protocol, two of the pig herds were chosen for triplicate sampling. These two herds were sampled three times on the same day (25 samples x 3 sampling rounds), resulting in six pooled samples (2 herds x 3 sampling rounds), from which the within-farm variation was assessed. A table with all samples and their metadata is included as Supplementary Table  1.
Bioinformatics processing The FASTQ files with sequencing data for each sample were analyzed following the principles from the previously described MGmapper tool. 15 The reads were first cleaned by cleaning out adaptors using BBduk (BBMap v39. 92 -Bushnell B. -https://sourceforge. net/projects/bbmap/), and by removing reads that aligned to the internal sequencing control phi-X174 as determined by the BWA-MEM algorithm. 19 Trimmed read pairs were aligned using the BWA-MEM algorithm Prokaryotic RefSeq genomes from NCBI GenBank (downloaded on Nov. 18, 2016) with the "reference" or "representative" genome tags. The BWA-MEM algorithm (v0. 7. 15) was slightly modified such that the insert size was estimated for the entire sample unaffected by the number of CPU cores used. This estimate was then used for re-analyzing the cleaned read pairs using BWA-MEM, ensuring that the thresholds for a read pair to map as a 'proper pair' were the same for all read pairs, and avoiding bad insert size estimates when few read pairs aligned.
The read pairs were aligned to the bacterial NCBI genomes again, and to the AMR genes present in the ResFinder database (accessed Nov. 17, 2016). 20 ResFinder is a manually curated database of horizontally acquired AMR genes and thus does not include intrinsic AMR genes and mutated housekeeping genes providing AMR by changing the drug target. Technical duplicate read pairs were then removed using 'MarkDuplicates' from the Picard command line tools (v2. 8. 3; http://broadinstitute. github. io/picard/). For each ResFinder reference sequence, we counted the number of read pairs that properly aligned with at least 50 bp aligning from both the forward and reverse reads. Each read pair matching a ResFinder reference was assigned to the first highest-scoring reference, as done in MGmapper. The same was done for NCBI microbial genomes in order to quantify the bacteriome and get a measure of the microbial proportion within each sample.
This total was used to normalize the ResFinder counts, by computing an FPKM-value (fragments per kilobase reference per million bacterial fragments) for each ResFinder reference sequence. The FPKM values were computed by dividing the mapping count on each reference with its gene length and the total number of bacterial read pairs for the samples and multiplying by 10 9. 21  mapping count data and their associated FPKM values can be found in Supplementary Table  2  and 3. Because ResFinder contains many representatives of certain gene families, a high degree of homology exists, with long stretches of the references being identical. This causes unspecific mapping between highidentity sequences. To eliminate this random noise, we chose to aggregate read counts and relative abundances post-mapping at higher levels based on sequence identity.
Functional resistance database 25] [26]  Briefly, in each of these studies, DNA was extracted from environmental and human fecal samples, fragmented and cloned into a plasmid vector and screened for AMR functionality in E. coli cultured with one of multiple antimicrobials. AMR-granting plasmid inserts were then sequenced and the responsible open reading frame was identified. The protocol for the database construction can be found at https://cge. cbs. dtu. dk/services/ResFinderFG/. Genes were quantified using MGmapper as was done for ResFinder. Genes with more than 90% identity to ResFinder genes were removed post mapping to obtain the new AMR genes without overlap with ResFinder. The resulting data was aggregated to 90% gene clusters, using CD-HIT-EST, as was done for ResFinder. 22 The most frequent gene clusters remaining were derived from genes selected using: trimethoprim, chloramphenicol, co-trimoxazole, cycloserine, amoxicillin, gentamicin, penicillin and tetracycline.
Principal coordinate analysis and resistome clustering For principal coordinate analysis (PCoA), the gene-cluster level FPKM matrix was Hellinger-transformed and the Bray-Curtis (BC) dissimilarities between all samples were calculated using the R package vegan. 27 PCoA was carried out for both pigs and poultry; combined and separately, using the vegan function 'betadisper'. The same analysis was used to test whether host animal and country were significant predictors of withingroup beta diversity dispersion. The effects of country on sample dissimilarities was determined using 'permutational multivariate analysis of variance using distance matrices' ('adonis2' function in vegan package), separately for pig and poultry.
Antimicrobial use in livestock Data for national livestock antimicrobial usage (AMU) was obtained from the European Medicines Agency's 2014 European Surveillance of Veterinary Antimicrobial Consumption (ESVAC) report and was stratified by major drug family. 28 The mass of active compound sold for use in animals in 2014 was divided by the Population Correction Unit (PCU) in 10 6 kg -approximating the biomass. The PCU is a unit that allows interspecies integration by adjusting for import/export and differences in average weight between species when they are most likely to receive antimicrobial treatment. The estimate was multiplied by 1000 to obtain drug mg/kg PCU livestock. The country-specific veterinary drug use can be found in Supplementary Table  5.
Procrustes analyses In order to test the association between country-specific AMU patterns and the resistomes, we performed Procrustes analysis using the vegan R package as follows. A PCoA was generated from Euclidean distances between the samples in the PCU-corrected AMU (Supplementary Table  5 , Supplementary Figure  1 ). The AMU PCoA was tested against the previously mentioned Hellinger-transformed, resistome BC dissimilarity PCoA using the 'protest' function with the default 999 permutations, again separately for pigs and poultry. The gene-cluster FPKM ResFinder matrix and the genus-level FPKM taxonomy matrix were Hellinger transformed and BC dissimilarities were calculated. They were ordinated using non-metric multidimensional scaling (NMDS) with the 'metaMDS' vegan function (999 permutations) for pig and poultry samples separately.
Visualization Heatmaps were produced using the pheatmap R package. For heatmaps showing individual gene abundances, the BC dissimilarities between samples were used. For all other dendrograms, the Pearson product-moment correlation coefficients (PPMCC) were used. Complete-linkage clustering was used for all dendrogram clustering. For sample similarities, BC dissimilarity was converted to a similarity percentage, i. e. , 100*(1-BC). The circular BC sample dendrogram was exported in Newick format using the ape package and further annotated with the Interactive Tree of Life tool. 31, 32 Bar-, box-and scatter plots were produced using the ggplot2 R library. 33 The R library RcolorBrewer was used to generate the color palettes used. The library is based on work by Cynthia A. Brewer (www. ColorBrewer.
Statistical analyses All statistics was done in Microsoft R Open (MRO) 3. 3. 2, using the libraries and procedures detailed below. Exact package versions can be found here: https://mran. revolutionanalytics. com/snapshot/2016-11-01/bin/windows/contrib/3. 3/. For statistical tests, only the first sampling from triple-sampled herds was included (see Supplementary Table  1 ). Unless otherwise mentioned, all statistical analyses were performed on pigs and poultry separately.
Effect of AMU on total AMR For testing the effect of total AMU on total metagenomic AMR abundance (sum of all genes), we used the lme4 1. 1-12 package to make linear mixed effects regression models with total livestock drug usage as the independent variable, total AMR abundance (FPKM) as the dependent variable and country as a mixed effect intercept, adjusting for the fact that AMR abundance observed in farms from the same country is correlated due to factors that are not included in this study. 34 The effect of AMU was modelled on a logarithmic scale, which resulted in lower Akaike's information criteria compared with modelling AMU on a linear scale. Country-and sample-level residuals were plotted and inspected for normality and homoscedasticity. Pig sample residuals and country residuals were normal and so were poultry country residuals.
Differential abundance analysis To identify differentially abundant AMR genes per country, we analyzed the read pair mapping count matrix using the DESeq2 package as previously recommended for metagenomic read count data. 35, 36 This was done on the raw read pair matrix, following recommendations that rarefying is not warranted in metagenomic studies. 36 The read-pair count matrices for pigs and poultry were analyzed separately. The number of mapped bacterial pairs was divided by the minimum number of mapped bacterial pairs and was used as the size factor. For each gene, we used a two-sided Wald test to determine whether the fold change between countries differed from 0 and extracted all the country-versus-country results. P-values were adjusted for the false discovery rate (FDR) using the Benjamini-Hochberg approach and we used a significance threshold of alpha: 0.
Differential abundance analysis In order to test which specific genes differed in abundance between countries, we carried out a differential abundance analysis for ResFinder gene read pair counts. Heavy overrepresentation of low unadjusted pvalues indicated a large effect of country in both in the pig and poultry datasets (Supplementary Figure  7 ). Of special interest was the newly characterized Enterococcus-associated linezolid-resistance gene optrA, that had a significantly higher abundance in Bulgarian poultry farms, compared with poultry farms in all other countries (FDR < 0. 05) (Figure  3b ). A single Spanish farm did, however, have even higher optrA abundance than any other farm. Among the pig herds, the optrA gene was more abundant in Bulgarian and Italian herds than anywhere else (except for two farms in Spain) (FDR<0.
Association between bacteriome and resistome To test the degree to which bacterial genus composition of the microbiota dictates the resistomes, Procrustes analyses were performed. We compared ordinations of the pig microbiome with the pig resistome and the poultry microbiome with the poultry resistome (Figure  5 ). We found that for both pig and poultry, the bacterial composition correlated significantly with the resistome (p<0. 001). Samples with similar taxonomic compositions thus tended to have similar resistome compositions. The correspondence between the two datasets was slightly stronger in pigs (Procrustes symmetric correlation: 0. 71) than in poultry (Procrustes symmetric correlation: 0. 66). Interestingly, in pig samples we saw a country effect on the strength of association between the bacteriome and the resistome.
Functional AMR genes In addition to using ResFinder, we also ran most analyses with the FRD database, to elucidate whether the functionally determined AMR genes behave similarly to the acquired AMR genes in ResFinder. If FRD genes serve similar AMR functionality as the acquired ResFinder genes, we would expect similar results. Using the FRD database, we found both similar and different patterns, compared with using ResFinder. There was still a perfect separation between pig and poultry samples, but the country separation in pigs was less distinct than when using ResFinder (Supplementary Figure  10 ). Though less variation could be explained by two axes, the PCoA plot of pig samples now clustered German and Spanish samples, with the remaining countries being more similar. The resistome richness showed similar patterns to ResFinder: Spanish, Italian, Polish and Bulgarian samples had a higher estimated richness in both pig and poultry, compared to the other countries.
E. coli in functional metagenomic assays, they might not provide AMR functionality in their natural hosts with natural expression levels. If most of them did, we could expect to see AM-based selection and an association to drug usage, like it is observed for the AMR genes in ResFinder -a database of genes known to provide AMR to their natural hosts. This finding echoes previous sentiments that one should carefully consider the risk to human health imposed by individual AMR genes. 41 Some FRD genes might represent high risk, but we currently do not know what subset that is. Creating the FRD is a first step in trying to catalog the many AMR genes found in functional metagenomic studies. Screening sequenced pathogenic isolates and metagenomic assemblies for FRD genes, would be a good start for assessing their host range and risk potential.
Acknowledgements We would like to thank all the anonymous pig and poultry herd owners who agreed to participate in the study and especially everyone involved in sampling and lab work: BE:  Marjolijn Schlepers. BG:  Teodora Ivanova ,  Nikolay Cholakov ,  Eva Gurova-Mehmedova ,  Krasen Penchev. DE: Franziska Nienhaus. DK:  Cecilie Liv Nielsen ,  Pia Ryt-Hansen ,  Bonnie Hølstad ,  Bettina Rasmussen ,  Kirstine Nielsen. FR:  Coton Jenna ,  Dorenlor Virginie ,  Eono Florent ,  Eveno Eric ,  Le Bouquin Sophie ,  Leon Denis ,  Thomas Rodolphe. IT:  Mario Gherpelli ,  Marco Pegoraro ,  Virginia Carfora. NL: Daisy de Vries. PL:  Beata Gawlik ,  Dorota Krasucka ,  Andrzej Hoszowski. The  EFFORT  project (www. effort-against-amr. eu) and the work presented here is supported by  EU ,  FP7-KBBE-2013-7 , grant agreement  613754.
Defining regions of interest (ROIs) in the visual cortex As noted above, grouping of features for group-lasso estimation benefits from a rational basis for defining the groups and here we exploit the existence of multiple functional maps in the visual cortex to comprise the basis for group formation. For purposes of the present analysis, we defined the detailed 3D shape of each of 18 visual ROIs in 25 participants (V1-L, V1-R, V2v-L, V2v-R, V2d-L, V2d-R, V3v-L, V3v-R, V3d-L, V3d-R, V4-L, V4-R, V3A-L, V3A-R, LOC-L, LOC-R, MT-L, MT-R). These definitions are based on high-resolution T1 anatomical scans combined with functional MRI scans. Structural and functional MRI scanning was conducted at 3T (Siemens Tim Trio, Erlangen, Germany) using a 12-channel head coil. We acquired a T1-weighted MRI dataset (3-D MP-RAGE sequence, 0.
8 × 0. 8 × 0. 8mm 3 and a 3-D T2-weighted dataset (SE sequence at 1 × 1 × 1mm 3 resolution) for tissue segmentation and registration with the functional scans. For fMRI, we employed a single-shot, gradient-echo EPI sequence (TR/TE = 2000/28 ms, flip angle 80, 126 volumes per run) with a voxel size of 1. 7 × 1. 7 × 2mm 3 (128 × 128 acquisition matrix, 220 mm FOV, bandwidth 1860 Hz/pixel, echo spacing 0. 71 ms). We acquired 30 slices without gaps, positioned in the transverse-to-coronal plane approximately parallel to the corpus callosum and covering the whole cerebrum. Once per session, a 2-D SE T1-weighted volume was acquired with the same slice specifications as the functional series in order to facilitate registration of the fMRI data to the anatomical scan. The research was reviewed and approved by the Institutional Review Board of Stanford University.