dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation database SSM measurements using TDR and hand-held thetaprobe were carried out on different wheat sites in Morocco and Tunisia, respectively. This database is used for the validation of the retrieval approach presented in this study. Data were collected from: i) one rainfed wheat located about 40 km east of Marrakech near the city of Sidi Rahal (hereafter named "Sidi Rahal") during two growing seasons  (2016 -2017 and 2017 -2018; (Amazirh et al. , 2018; Ezzahar et al. , 2020) ; ii) 18 plots located in the Kairouan plain in Tunisia during the 2016-2017 growing season  (Bousbih et al. , 2017). General information about all plots is provided in Table  2. Over the Moroccan field, measurements are taken every 30 min thanks to the TDR probes while 20 thetaprobe samples are averaged per field for the Tunisian plots at the time of the Sentinel-1 overpass (between 30 min before and 2h after for the ascending and descending orbits, respectively).
Please note that it is expected that the validation database covers a large variability of wheat crop conditions representative of the south Mediterranean area. Indeed, it covers rainfed and irrigated fields with different technique (sprinkler); owners are different, in particular for the 18 fields in Tunisia. Consequently, the agricultural practices differ depending on the experience of the farmer. ''Domaine Rafi'' fields are well managed in terms of irrigation as the farmer follows the FAO-56 recommendation  (Allen et al. , 1998)  while irrigated fields in Tunisia belong to different smallholders farmers whose irrigation scheduling may be more empirical by lack of objective scientific information. Likewise, nutrient inputs are used on irrigated fields of ''Domaine Rafi'' while no inputs are applied to rainfed fields.
Across the swath, the incidence angle ranges between 31° and 46°. Level 1 products can be directly downloaded from the Sentinel-1 Data Hub website (https://scihub. copernicus. eu/). Two products types were used in this work: GRDH (Ground Range Detected High resolution) for backscattering coefficient and SLC (Single Look Complex) for computing interferometric coherence. Table  3  summarizes all the images used within this study.
Sentinel-2 optical data ESA's optical Sentinel-2 satellites S2A and S2B were also launched on June 2015 and March 2017, respectively. Sentinel-2 provides images every 5 days for 13 spectral bands at a resolution ranging from 10 to 60 m according to the spectral band. Level 2A images were processed and provided by the National Centre for Space Studies (CNES) through the PEPS platform free of charge within the frame of THEIA (https://theia. cnes. fr/). Data were corrected from atmospheric effects using the MAJA processor designed at CESBIO  (Hagolle et al. , 2015). 10 of the 17 images (S2A) were cloud-free for the first agricultural season (November 2016 to May 2017) while 25 products were retained among 33 available for the second season (November 2017 to June 2018) on the two fields (Field 1 and Field 2) of "Domaine Rafi".
In addition, it is also assumed that ℎ variability from one field to another is small considering also the period from sowing to harvest. Within this context, a constant ℎ is sought for the whole crop season with ℎ values varying from 0. 7 cm to 1. 5 cm with a step of 0. 05 cm. The range of ℎ has been determined from the in situ database and from a literature review  (Balenzano et al. , 2011; Bousbih et al. , 2017; Mattia et al. , 2003; Picard et al. , 2003; Wang et al. , 2018; Zribi et al. , 2011). The average of all the retrieved SSM time series is then kept as the solution to the inverse problem. Please note that a constant value of 1 cm was first used. Keeping the average of SSM time series corresponding to different values of soil roughness provided with slightly lower biases (not shown) than with the unique ℎ value of 1 cm.
2° using VV backscattering coefficients on Field 1 and Field 3 with r above 0. 60 apart for the empirical approach based on Δσ 0 in Field  1 (r=0. 38 at 35°2 and VV polarization). By contrast, SSM for most of the methods and, in particular for those based on the WCM, performed poorly on Field 2. For information, for the WCM based approaches, the retrieval on Field 2 has also been tested by calibrating the WCM on the second season or by using the calibrated coefficient of Field 1. In both cases, poor results were obtained. This is attributed to very specific growing conditions that severely affected the wheat development as already discussed (see pictures Fig. S1  and  In order to evaluate the genericity of all methods, they have been implemented on the validation database using the calibration carried out on Field 1 and Field 2 during the first crop season.
By contrast, overall performances are worse for the empirical approaches but they appear more stable from one site to another, in particular for the σ 0 based method as demonstrated by the small changes of correlation coefficient ranging from 0. 60 to 0. 62 and of RMSE (0. 07 m 3 /m 3 ). Despite the moderate loss of performance from the fields of "Domaine Rafi" to the validation sites, the method based on the -AB relationships provided with the best results on the validation database with systematically higher correlation coefficients, lower RMSE and biases remaining low with a maximum value of 0. 03 m 3 /m 3 for VV polarization and an average bias of 0. 01 m 3 /m 3 on the entire database. For illustration purpose, Fig. 9  displays the scatter plot in the rainfed field, Morocco (a and b) and in the Kairouan plain, Tunisia (c and d) for the latter approach.
Fig. 9e  and f  shows the scatterplots for the whole database including Field 1 and Field 3. In order to evaluate the potential loss of performance associated to vegetation growth, the correlation coefficients of the seven methods are plotted as a function of AGB in Fig. 10  using data of Field  Finally, the ;<% C DEF was implemented to map SSM over an irrigated perimeter located 40 km east of Marrakech name R3  (Duchemin et al. , 2006)  for illustration purpose. The perimeter is dominated by wheat even if some vegetables and trees are also cropped. approaches require additional measurements of the vegetation characteristics to develop the relationships between PR/ -AGB/VWC adding three parameters to be fitted with regards to WCM &' ( , ($$%) and ∆ (∆$$%) methods. The main results can be summarized as follows: -All methods performed reasonably well at VV polarization with better results for the WCM-based when compared to the empirical ones (σ 0 and Δσ 0 ).
-Despite a moderate variability of the statistical metrics when going from one site to another, the new method based on the relationship between the interferometric coherence and the AGB provided with the best results with a correlation coefficient r of 0. 74 and a small bias of 0. 01 m 3 /m 3. -By contrast, overall performances were worse for the empirical approaches but they were more stable from one site to another, in particular for the σ 0 based method. The proposed new approaches taking advantage of two complementary and relatively independent information derived from Sentinel-1 data made it possible to get away from dependence on optical data that are restricted by weather and illumination conditions. They are valid for wheat crops (as the relationships between the interferometric coherence/PR and vegetation characteristics were established for wheat) during the period from seeding after all soil tillering action to harvest only meaning three conditions must be satisfied:  (1)  soil roughness parameter ℎ remains within the range from 0.
7 to 1. 5 cm; (2) there is no drastic change of soil roughness during the crop season. Stated differently, no soil tillering is done from seeding to harvest; ; (3) the sensitivity of interferometric coherence to soil moisture change is of a 2 nd order with regards to AGB and VWC changes; (4) they reflect the sensitivity of the radar signal to the vegetation geometrical structure, which can induce significant errors if it is not well taken into account (explaining low results over the second test field ). The limited loss of performances on the validation database of the coherence based technique points out that it could be applied with a reasonable accuracy on sites outside of the area where it has been developed. This question of the genericity of the retrieval approach is fundamental. Very similar wheat varieties are sown in the south Mediterranean region meaning that the canopy structure are similar.
Likewise, the fact that the phenological stages are very close as all the fields in our database are located in the South Mediterranean region lead to similar development of the plants. A strong change of structure would probably impact negatively the results. This means that the method would not be valid for different crop and in particular row crops such as sunflower. Even for wheat field, specific conditions such as the development of adventices with a different structure of wheat or stems laid down by the wind degrade the performance of the approach as demonstrated by the poor results obtained on Field 2. Further validation is thus required. Further investigations will focused on the combination of C and L-band data within the frame of the numerous L-band mission in preparation (ROSE-L, NISAR) or already into orbit (ALOS-2, SAOCOM) for the joint retrieval of SSM and vegetation biomass, potentially separating its stems and heads components.
part of the field were almost completely covered around vegetation peak; (2) Field 2 during the second year was more exposed to wind than Field 1 because the density of seeding was much higher and because a mix of wheat and barley were seeded by mistake. Barley is characterized by longer stems than wheat that grown up to 146 cm on April 2018 thanks to irrigation. By contrast, the height of stems on Field 1 seeded with pure wheat, only reached 110 cm. Stems were almost completely laid down by the wind from April 12, 2018 (see picture S2) on Field 2. Considering these very specific growing conditions of Field 2 during the 2 nd year, a third field named Field 3 (Fig. 1), was also instrumented during 2018-2019 to extent the validation database under normal growing conditions. It was seeded on November 4, 2018 and harvested on June 06, 2019.
by minimizing the RMSE between the predicted and the observed backscattering coefficient; (2) the empirical parameters of the relationships between coherence or PR and the vegetation characteristics. and parameters were estimated separately for each radar polarization following Baghdadi et al. (2017) and Hosseini and McNairn (2017). The second year (2017-2018) on Field 1 and 2, field 3 data and the validation database are used for validation. Please note that it was decided to keep the data from the 2 nd season of Field 2 for the method evaluation when wheat faces very adverse growing conditions as already described.
Paired-end reads were demultiplexed by index using oninstrument software and by barcode using a custom python script  (Quigley et al. , 2018). Paired-end reads were merged, trimmed of primer sequences, and quality filtered  (Eren et al. , 2013). The final datasets served as input to Minimum Entropy Decomposition (MED) analysis  (Eren et al. , 2015). The MED analysis returned 17,553,478 high-quality, merged V4 reads. The average number of reads across the 226 samples was 77,670 (±48,693 [SD]), ranging from 13,578 to 485,446 reads. We created a reference V4 16S rDNA database from the SILVA reference taxonomy v. 132 1   (Pruesse et al. , 2007). The MED analysis identified 6,320 ASVs (amplicon sequence variants) that had ≥ 10 assigned reads across all sequenced samples. We subsampled without replacement (rarefied) to an even sampling depth of 17,000 highquality, merged sequences/sample.
Taxonomy was assigned to these ASVs using VSEARCH  (Rognes et al. , 2016)  and our custom V4 database. To improve taxonomic resolution, assignments with poor identification (e. g. , "uncultured bacterium") were amended by searching the NCBI non-redundant nucleotide BLAST database in addition to the SILVA reference, examining the taxonomic distribution of the matches, and creating a placeholder taxonomy based on the highest taxonomic level (e. g. , "Proteobacteria_cl_or_fam_gen" would represent an unknown genus within the Proteobacteria). We refer to ASVs by concatenating their assigned ASV number to their genuslevel taxonomy (e. g. , Granulosicoccus_3260). Raw fastq files of sequences can be found at the NCBI Sequence Read Archive, accession number PRJNA481673.
A Priori Hypothesis Testing All statistical analyses were performed using R v. 3. 6. 1 (R Core Team, 2019). Code used for performing each analysis can be found at www. github. com/kacf24/FucusTransplant. To evaluate our hypotheses based on community composition and structure we used subsets of our data to create multivariate generalized linear models (MGLMs;  Wang et al. , 2012). Models based on community composition were created by transforming abundance data into a presence/absence matrix and assuming a binomial distribution for the model, whereas community structure models used untransformed abundance data and a negative-binomial distribution  (Roth-Schulze et al. , 2018). Separate models were created to examine differences in bacterial communities between (1) species and tissues in unmanipulated (natural) Fucus spp.
Verification of Relative Environmental Stress Levels Between the High and Mid Zones Although the high intertidal zone usually has higher levels of environmental stress than the mid zone, we verified this in iButton records were linked to the tidal height recorded at a nearby NOAA buoy (#8413320, Bar Harbor, ME, United States), and time series using these data were then filtered to contain only temperatures of daytime exposures to compute descriptive statistics. Hierarchical clustering visualized similarities between iButtons based on Euclidean dissimilarity (pairwise differences) of Z-Score normalized iButton records. Records were then clustered using Ward's sum of square errors method  (Suzuki and Shimodaira, 2006; Murtagh and Legendre, 2014)  to verify expected levels of relative stress between the mid zone and high zone.
Response of Bacterial Communities to Transplant of Mid-Zone F. vesiculosus to the High Zone Changes in Bacterial Communities on Vegetative Tissue Over Time (Hypothesis 4) Significant differences (Table  1C ) in bacterial communities on vegetative tips over the three sampling dates were found to be related to sample area, treatment (PC, W, D), and the duration (day) of treatment (Hypothesis 4), along with a day:treatment interaction. However, pairwise testing comparisons were  largely non-significant for both composition and structure (Supplementary Figure  S3 ). The only significant comparison was between D on 20 July and PC on 6 July (Supplementary Figure  S3 ). Inspection of the NMDS (Figure  7 ) shows a higher degree of variability in datasets from July 20 compared to July 6. There appear to be two distinct groups of datasets that are driving this variability and are a mixture of both D and W samples, but the groups come from the different sample areas (Figure  7 ).
ACKNOWLEDGMENTS We thank the  U. S. National Park Service  for permission to conduct the research in Acadia National Park (Permit:  ACAD-2016-SCI-0010 ) and  University of Maine  undergraduates  Margaret Aydlett  and  Olivia Barberi  for help in the lab and field. SB appreciates discussion with  David Warton  ( University of New South Wales ) about linear models, general linear models, and mvabund. We are grateful to the editor and reviewers for their insightful comments.
This not only challenges the concept of a linear trajectory of eGFR decline but also conceptually creates a dilemma for biomarker research. To identify prognostic classifiers, observational or interventional studies are used, which follow patients for a specific period of time. Individuals are allocated to prognostic groups based on their progression characteristics (e. g. the decrease of eGFR) at the end of the observation period and baseline data are assessed to separate the strata. The internal and external validity of the findings however critically depend on the assumption that individuals remain in their category of prognosis irrespective of the further follow-up time. In case the course is not linear (e. g. based on variable pathophysiology and/or effects of therapy) we can expect patients changing their prognostic group, which will directly affect the accuracy of a prognostic biomarker (panel).
The objective of this study was to provide a descriptive analysis of the intra-individual variability in eGFR trajectories in early DKD. Common surrogate endpoints used in published biomarker studies (for example in  [19] [20] [21] [22]  on DKD progression were examined regarding their stability over 5 years of follow-up. We analyzed data from two independent prospective cohorts. Based on various definitions of the change in eGFR between baseline and a specific point during clinical follow-up, patients were allocated to a group with a detrimental prognosis. Next, we evaluated how many of these individuals met the respective definition also at other time points of follow-up. In case of a stable eGFR trajectory the group with a detrimental prognosis should be composed of the same individuals over time whereas intra-individual non-linearity of progression will result in the contrary.
73m 2 /year to define patients at highest risk and their early identification becomes a more realistic aim. Biomarker research in this area conventionally relies on information from observational cohorts or interventional trials and allocates individuals to a high risk/fast progression group. Next, baseline data are analysed to define a biomarker/a biomarker panel that separates these individuals from controls/individuals with slow progression. Unfortunately, the success to predict changes in eGFR by biomarkers hitherto is limited. One explanation is their often limited, additional information when used on top of albuminuria and eGFR. This could be a consequence of a bad markers selection and much effort focuses on "Omics"-profiling and machine learning techniques to advance, but again only little progress has been achieved so far and the cost effectiveness remains to be determined  24.
Methods Study population. For evaluating the decline of eGFR in type 2 diabetes we used data from patients recruited into the "Prospective cohort study in patients with type 2 diabetes mellitus for validation of biomarkers" (PROVALID). Details of the trial are presented elsewhere  30. In summary, the PROVALID database provides annual information on medication and the incidence and progression of renal and cardiovascular disease in 4000 prevalent patients with type 2 diabetes in five European countries (Austria, Hungary, the Netherlands, Poland and Scotland) all being taken care at the primary level of healthcare. We selected 860 individuals with complete annual follow up information for at least 5 consecutive years. Exclusion criteria were an age < 18 years, a BMI < 18 or > 40 kg/m 2 and a baseline eGFR value > 150 ml/min/1.
Acknowledgements We thank  Manfred Hecking, MD, PhD , for critically reviewing the manuscript. This project has received funding from  Abbvie  and the  Innovative Medicines Initiative 2 Joint undertaking  under grant agreement No. 115974  ( Beat-DKD ). This Joint undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA  with  JDRF.
Molecular Identification of the Bacterial Isolates Total genomic DNA was extracted while using a Genomic Mini AX Bacteria Spin Kit (A&A Biotechnology, Gdynia, Poland and then stored at -20 • C. All of the PCR amplifications were carried out with PCR mix RAPID (A&A Biotechnology, Gdynia, Poland), according to the manufacturer's recommendations. The primer sequences and PCR conditions used are listed in Table  3. The amplified PCR products were purified with Clean-Up purification columns (A&A Biotechnology, Gdynia, Poland) and then sequenced in Genomed S. A. (Warsaw, Poland). Preliminarily, the 16S rRNA gene sequences were compared with the EzBioCloude database. All of the obtained sequences were analyzed while using BLAST available on the NCBI website. Multiple sequence alignment matrices of the individual gene sequences were created using ClustalW included in the MEGA 6.
06 software  [56]. The sequence identity values were calculated while using BioEdit 7. 0. 5 software. The 16S rRNA gene sequence similarity threshold value of 98. 7% between the isolate and species type strain was used as an indicator that an isolate can be a member of a given species  [34]. The identification of the isolates at the species level was considered to be final when the searching results that were based on 16S rRNA gene sequences were concordant with those based on the rpoB, rpoD, or recA gene sequences.
Phylogenetic Analysis Phylogenetic analyses of gene sequences were performed to confirm the identification results. The 16S rRNA, rpoB, rpoD, and recA gene sequences of isolates that were obtained in this study were compared to GenBank nucleotide sequences using BLAST (NCBI). Multiple sequence alignments were created using ClustalW at the default configuration and manually checked. The evolutionary distances were computed using the Tamura-Nei algorithm and the phylogenetic trees were generated using the.
Acknowledgments: The authors gratefully acknowledge use of the services and facilities of the  Center for Interdisciplinary Research of The John Paul II Catholic University of Lublin, Lublin, Poland , co-funded by the  European Union  from the  European Regional Development Fund  in the frame of the  Operational Programme Development of Eastern Poland 2007-2013  ( POPW. 01. 03. 00-06-003/09-00 ).
Gene Accession Numbers Strain with the Highest Similarity to the Isolate in the Gene Bank Based on the Gene Sequence/Gene Accession Number/% Nucleotide Identity 16S rDNA rpoD/rpoB recA MT3 L2 05 Pseudomonas chlororaphis 16S rDNA-MM421924 rpoD-MN445046 P. chlororaphis subsp. piscum JF3835 T (FJ168539)-99. 8% * P. chlororaphis subsp. piscum ZJU60 (CP027656)-98. 2%; P. chlororaphis subsp. aurefaciens NCIMB 9265 (AB039555)-98. 2% n. d. MT3 L2 08 Pseudomonas chlororaphis 16S rDNA-MM421925 rpoD-MN445047 P. chlororaphis subsp. piscum JF3835 T (FJ168539)-99. 8% * P. chlororaphis subsp. piscum ZJU60 (CP027656)-98. 2%; P. chlororaphis subsp. aurefaciens NCIMB 9265 (AB039555)-98. 2% n. d. Pseudomonas chlororaphis MT3 16S rDNA-MM421926 P. chlororaphis subsp. piscum JF3835 T L2 10 rpoD-MN445048 (FJ168539)-99.
Gene Accession Numbers Strain with the Highest Similarity to the Isolate in the Gene Bank Based on the Gene Sequence/Gene Accession Number/% Nucleotide Identity 16S rDNA rpoD/rpoB recA MTB1 L3 12 Serratia liquefaciens 16S rDNA-MM422010 rpoB-MN445052 Serratia quinivorans DSM4597 T 042062)-98. 9% (AJ233435)-99. 4%; S. liquefaciens CIP 103238 T (NR ** S. liquefaciens LMG7884 T (JX425335)-99. 4% S. quinivorans LMG7887 T (JX425309)-97. 7% n. d. MTA3 L2 13 Acinetobacter calcoaceticus 16S rDNA-MN429316 rpoB-MN445056 A. calcoaceticus DSM 30006 T (AJ633632)-99. 8% ** A. calcoaceticus CIP 81. 8 T (DQ207474)-97. 9%; A. calcoaceticus CA16 (NZ_CP020000)-98. 7% n. d. MTS3 L3 15.