dataesr/scientific-paragraphs-categorization huggingface dataset:
The comparison between altimetry data and in situ records enabled to correct or fulfil in situ measurements. RL1 data were checked against two VS (VS1_564 and VS2_564) that bracket the gauge, and RL2 was checked against VS1_149 and VSR52. The difference between observed WL and WL linearly interpolated from the framing VS was used to update in situ gauge levelling when necessary. The expected precision for the consolidated RL1 and RL2 gauges is ±0. 15 m. However, we expected this accuracy to be degraded during exceptional droughts (2010) when gauges remained out of water. RL1 WL reflects the main channel WL, starting to increase early in November until June when the recession phase is initiating. The mean RL1 WL peak is 23. 30 m (SD= 0. 90 m) over 2006-2015 period. The average tidal amplitude over the same period was 11.
Bottom friction map Following  Rudorff et al. , 2014a , we used the dual-season wetlands map of Amazon Basin  (Hess et al. , 2015)  to define zones with same cover assumed to reflect friction properties in the FP. This product is available at the NASA Earth Observing System Data and Information System (EOSDIS) website (http://reverb. echo. nasa. gov/). It is based on mosaicked L-band SAR imagery, acquired by  JERS-1 (1992 JERS-1 ( -1998) )  at low water and high water seasons during the 1995-1996 HY. Hess et al. , (2015)  estimated the accuracy of the wetlands mapping to 93% when compared with high-resolution videography samples. From this map, we retained 4 distinct classes: permanent water, shrubs, flooded forest and forest in spatial proportions of 44%, 14%, 21%, and 20%, respectively. Permanent water is likely overestimated as the wetlands map of the low water have been acquired during falling water  (Pinel et al.
Topography and bathymetry data Both the hydrologic and hydrodynamic models required accurate topography data. In the Amazon, the most reliable source of topography data is originating from the Shuttle Radar Topographic Mission (SRTM), but it presents bias  (Carabajal & Harding, 2006; Rodriguez et al. , 2006). We used the topography dataset produced by  Pinel et al. , (2015)  for the study area. This dataset was derived from SRTMGL1 (30 m-resolution) dataset. The authors proposed to correct and improve as follow. The authors estimated the interferometric bias comparing SRTMGL1 elevation against ICEsat GLAS data extracted over non-vegetated regions and bathymetric data acquired at high WL. This bias was homogeneously subtracted to SRTMGL1 DEM. The authors combined the dual-season wetland map produced by  Hess et al.
Flood extent from ALOS/PALSAR imagery Evaluating the model performance requires FE observations. Due to limitations in retrieving water under forest, product based on optical imagery such as the high-resolution map of global surface water  (Pekel et al. , 2016)  were not adapted to this study. As an alternative, Lband Synthetic Aperture RADAR (SAR) permits this detection  (Arnesen et al. , 2013; Ferreira-Ferreira et al. , 2014; Frappart et al. , 2005; Hess et al. , 2003; Martinez & Le Toan, 2007). Chapman et al. , 2015 released a regional water mask based on an automatic classification from ALOS SCANSAR imagery, a JAXA mission that operated from 2006 until 2011. Unfortunately, this product presented weaknesses for this study: coarse resolution (3 arcseconds), few available scenes (9 dates spread out on 2007-2008), covering few WL (48% of the WL variations), and a high grade of noise (average 17 % of the watershed present no data value).
JAXA provides the ALOS-1/PALSAR data at a treatment level in which the three first steps are already performed. The backscattering analysis and pixel classification were carried out using a manual thresholding method on the backscatter coefficients. During low water, with the low level of flooding, flooded pixels (open water such as main riverbeds or lakes) have low backscatter values and appear in black. Conversely, at high water, the signal of flooded pixels is a combination of open water (low backscatter values) and water under vegetation (high values backscatter resulting from the double-bounce effect). Hence, to map the presence of open water, we defined a single threshold at low water, while, we combined two thresholds during other hydrological period: one for the mapping of open water and one for vegetated water.
The fifth step consists in imposing consistency between classified images to reduce the number of misclassified pixels. Hence, a pixel classified as "water" in an image with a given WL had to remain classified as "water" in any image associated with higher WL. Conversely, pixels classified as "non-water" had to remain classified "non-water" in images taken at lower WL. Finally, we controlled the classification quality against the FEs issued from Chapman et al. , 2015. We compared scenes whose WL were similar (<0. 20m) and belonged to the same period (a total of 4 scenes). This analysis revealed a good agreement between both datasets (TS>61). Discrepancy can be partly attributed to the coarser resolution that increase the signal mixture in the pixels.
Velocity data Averaged velocities of cross-sections were measured with an Acoustic Doppler Current Profiler (ADCP) (RDI instrument 1200 Hz) at several periods of the water year at the Terra Alta (TA) location (3. 370°S, 60. 214°W) and RL2 gauge location (Figure  1b ). These two locations permitted measuring the channel discharges within a short distance from RL2 gauge. TA is located 3. 10 km upstream from the RL2 WL gauge on the connecting channel. Each measure is based on 4 passes of each channel cross-section. At last, the velocity dataset consisted of 30 measures collected during the 2006-2011 period. This dataset covered all hydrologic periods. The expected accuracy in optimal conditions (laboratory) is ± 2 mm. s -1  (Teledyne RDI, 2013). The model performance in terms of velocity was appraised from the comparison between simulated and observed velocity in the connecting channel using statistics previously described.
Besides, the quality of the observation datasets should slightly lower during low water: i) the RL1 and RL2 could have remained out of water ii) the probability of mixed water-non water pixels increases degrading the quality of ALOS/PALSAR images classification. In addition, at the lowest resolution (30 m) in the PLR mode, a higher number of mixed pixels should be encountered. During high water, our analysis highlighted slight vertical and horizontal underestimation that we associate to little inaccuracies of bank elevations or locations for the WLBC, leading to slight overflow underestimation. The model was able to simulate the CC-velocities variations. It also reproduces that the channel acts as inlet only during the early rising period and that overflow is the main water inflow pathway for the Janauacá FP.
Once overflow occurs, flow is reversed in the channel and the FP exports water into the main channel, as already mentioned in  Bonnet et al. , (2017). The overflow period also corresponds to the maximum exportation flow, even while FP WL is still increasing. However, the model delays the overflow onset of roughly 15 days, more likely because levee elevation is slightly overestimated. Levee overestimation also induces a slight overflow underestimation that is reflected in the computed velocities at high water. Accurate levee elevation is difficult to obtain from the available topography dataset (SRTM) but is crucial to correctly represent river-FP exchanges. Here, it was deduced from visual inspection of ALOS/PALSAR images and main channel WL.
Limitations and recommendations The analysis of the model performance evidenced the topographic controls. Hence, modeler should take into account both the accuracy and the resolution of input topography. Here, visual inspection of ALOS-1/PALSAR images allowed detecting several channels whose width remains inferior to the minimum mesh edge size (23 m). We might expect large improvement of DEM from the new generation of remote sensing products, such as Sentinel constellation. Sentinel 1/2 acquired data offers better spatial resolution (10 m against 30 m for Landsat) and acquisition frequency (one image every 12 to 6 days) that could enable to construct a much more realistic DEM and accurately detect the levee elevations. A method for integrating variable flood extent data with DEM is needed, but this technique is not yet established.
In addition, forested wetlands present pronounced microtopography that consists of hummocks and hollows  (Day et al. , 2007)  that obviously affect the local water velocity field. With a 23mmesh resolution, the model likely does not capture such fine-scale topographic controls. However, these features are taken into account through the roughness coefficient that is adjusted during the model calibration. This study highlighted the necessity of controlling the quality of the validation datasets. Indeed, both observed WL and FE suffer from degradation at low water preventing robust validation at that period. As in most of hydrodynamics studies, simulated velocity fields cannot be fully (spatially and temporally) validated with observations. Nevertheless, velocities validation step indicates that CC-velocities are correctly estimated giving confidence for the other FP domains.
Acknowledgement Meteorological data and main channel stage were obtained from the Brazilian National Water Agency (http://hidroweb. ana. gov. br/) and the Brazilian national meteorological institute (www. inmet. gov. br/). Remotely sensed product can be freely assessed i) at the Alaska Satellite Facility website (https://vertex. daac. asf. alaska. edu/) for the ALOS-1/PALSAR images, ii) at the Center for Topographic studies of the Ocean and Hydrosphere (http://ctoh. legos. obsmip. fr/) website for the altimetry data. The processed data used in this work, including observations of floodplain stage, channel velocity exchanges and bathymetry data can be accessed through the following link http://dx. doi. org/10.
For example, microarrays and sequencing technologies measure miRNA expression levels under various experimental conditions, to provide data that are often converted into differential expression levels  (2) (3) (4). Similarly, phenotypic high-content screening has led to large functional datasets  (5). The common questions are then how to interpret these tables, and which miRNAs to select for further evaluation and validation. In the following, we define as 'hits' those miRNAs with high scores that indicate their potential interest. These can be differentially expressed miRNAs, highly expressed miRNAs, or miRNAs with high scores or small P-values. To help in the analysis of such long lists of miRNAs, we have designed and built a free-to-use webserver to visualize and interpret miRNA datasets, entitled miRViz (http:// mirviz.
prabi. fr/). With miRViz, users can visualize their own and/or pre-loaded miRNA datasets on predefined miRNA networks, with various options to highlight or hide subsets of the data. These operations are accessible through the top menu on the left bandeau. The data-processing methods, such as data normalization, are not implemented in miRViz; these analyses should be conducted before using miRViz. For example, analysis for differential expression should be performed with one of the numerous available tools, such as DESeq2  (6) , before visualization in miRViz. MiRViz is designed to be as intuitive as possible. Additionally, the miRViz help file ('Download help', topright) guides users, click by click, together with a downloadable video tutorial. While a few studies, including our previous study, have proposed miRNA-network approaches  (7, 8) , to the best of our knowledge, none of them have proposed a dedicated tool for biologists to interpret miRNA datasets.
There are tools to build miRNA-mRNA target networks, including miRNet (http://www. mirnet. ca/), although the users have to previously identify miRNAs of interest from their datasets  (9). MiRViz and miRNet can thus be used in sequence. Bracken et al. reviewed the websites for enrichment analysis  (10). These websites are also complementary to miRViz, and can be used with the same datasets. For example, TAM (http: //www. lirmed. com/tam2/Home/) and miEAA (https://ccb-compute2. cs. uni-saarland. de/mieaa tool/) perform ontology enrichment at the level of miRNAs, and miRPath (http://snf-515788. vm. okeanos. grnet. gr/) and miRPathDB (https://mpd. bioinf. uni-sb. de/) do so at the level of the mRNA targets  (11) (12) (13) (14). This paper is organized as follows. In the following section, we present the different miRNA networks and the preloaded datasets.
Implementation MiRViz is composed of a JavaScript front-end server and a Java back-end server. While the best user experience is provided when running miRViz on wide screens with high resolution, it still remains of interest with lower resolution, where zooming out may sometimes be necessary to avoid overlapping components (for more details, see the Troubleshooting section of the downloadable mirviz-help. pdf). User datasets are not uploaded to the server but are safely loaded into the local browser. MiRViz is freely available without login requirements.
MicroRNA networks MiRViz is built around predefined miRNA networks, in which each node is represented by a circle that corresponds to a unique mature miRNA. In its 2020 version, miRViz can be used to visualize miRNA data from 11 different species, including human (hsa), mouse (mmu), Caenorhabditis elegans (cel) and Drosophila (dme). The architecture of the webserver was designed to easily add new networks and new species in the future. In miRViz, we propose eight different predefined networks. Each of the networks has its own rules for the connection of the miRNA nodes. The first network used by miRViz, 'Seed2 7', allows direct visualization of miRNA families by connecting pairs of miRNA nodes that share the same seed sequence. The two miRViz networks entitled 'Genomic Distance' connect neighboring miRNA genes on the genome.
Pre-loaded datasets Three miRNA tables are pre-loaded in miRViz to ease comparisons with user datasets: 'hsa miRNAmine cells', 'hsa miRNAmine tissues' and 'hsa TissueAtlas'. For the two first datasets, the data were gathered from miRNAmine  (3). For the third dataset, the data were gathered from Tis-sueAtlas (4). MiRNA expression was transformed into log 2 scales, and then averaged across all of the experiments performed under the same conditions (the number of different experiments used to average is indicated in brackets). Expression on a log 2 scale spans from 0 to 20.
Experimental microRNA datasets We have used various public miRNA expression datasets in the practical examples provided (from both microarrays and sequencing) to highlight miRViz strength  (19, 20). We have also used an in-house functional miRNA screening dataset  (21). The datasets are provided as Supplementary Tables and the data pre-processings are detailed in the Supplementary Materials. Briefly, the first dataset relates to miRNA sequencing, from the public identifier SRA106214. It provides miRNA expression levels in colon cancer cell line LIM1863, and in their secreted small vesicles, together with differential expression between vesicles content and the parental cells (Supplementary Table  S1 ). The second dataset provides the prognostic value for overall survival of patients with adrenocortical carcinoma, for each individual miRNA, expressed in the log 10 of the P-value obtained after log-rank test (Supplementary Table  S2 ).
The third dataset Downloaded from https://academic. oup. com/nar/advance-article-abstract/doi/10. 1093/nar/gkaa259/5823705 by CEA SACLAY, laurent. guyon@cea. fr on 28 April 2020 provides the differential expression between human embryonic stem cell and differentiated cells from the public dataset GSE14473 (Supplementary Table  S3 ). The fourth dataset provides the change of the percentage of breast cancer stem cells after transfection of individual miRNA mimics (Supplementary Table  S4 ). The last dataset presents the enrichment of two selected gene ontologies among predicted mRNA targets for each individual miRNA ('regulation of gene expression' and 'small-GTPase-mediated signal transduction', Supplementary Table  S5 ).
MiRViz highlights the selective export of the miR-320 family into colon cancer cell exosomes The first network used by miRViz, Seed2 7, allows direct visualization of miRNA families by connecting pairs of miRNA nodes that share the same seed sequence. The Seed2 7 networks can be visualized through miRViz for all of the 11 species proposed. As a demonstration of how miRViz can be used to interpret miRNA expression datasets, a few click tutorials are provided in the help file (top-right button on the website), which shows how to visualize tissue-specific miRNAs. Here, we propose a complementary example using the publicly available miR-seq dataset that profiles the LIM1863 colon cancer cell line and three different sorts of extracellular vesicles isolated from the culture supernatants  (19). Figure  1  shows the differentially expressed miRNA families in two types of immunoaffinity-isolated exosomes (i.
MiRViz can quickly identify these families, and provide a way to share the result. The hsa-miR-378/422a family is exported specifically in immunoaffinity-isolated A33 exosomes (P-value = 0. 008; paired Wilcoxon test; Figure  1A ). The hsa-miR-320 family is significantly exported in both sorts of exosomes (P-value = 0. 002; paired Wilcoxon test; Figure  1A, B ). This export is clear only for exosomes (i. e. not for small vesicles), and stronger for the A33 exosomes. We have confirmed these data by independent RT-qPCR measurements (Supplementary Figure  S1 ). Finally, all of the five expressed members of the hsa-let-7-3p/miR-98-3p family are significantly exported in all of the three types of extracellular vesicles (P-value = 6 × 10 -5 ; paired Wilcoxon test; Figure  1 ). In addition to overall aggregation behavior of miRNA hits, one can ask how significant hits are aggregated in a given family.
High expression of miRNAs of the Xq27. 3 cluster is predictive of better prognosis in adrenocortical carcinomas Both Genomic Distance networks can be visualized with miRViz for all of the 11 species. To demonstrate the interest of the Genomic Distance 50k network in the context of cancer, we reanalyzed the public data from Assie et al. (24) , which contains both the miRNome of tumor samples from patients diagnosed with adrenocortical carcinoma and their overall survival (OS) information. For each miRNA, the patients were separated into two groups of equal size, which depended on the miRNA quantification, and a Pvalue was calculated on the OS after log-rank tests. A hit is defined here as an expressed miRNA (median expression among patients > 10 reads) for which the P-value is <10 -2. Figure  2A  shows the Kaplan-Meier curves of two microR-NAs of interest, together with the P-value of the log-rank test, and the node colored according to the P-value.
MiRViz visually identifies the miR-302/519 stem-cell family in the regulation of breast cancer stem cell equilibrium As proof of purpose for the Diana50 network, Figure  3A  shows the differential expression of miRNAs in human embryonic stem cells that were cultured under two different conditions that favor either pluripotency or differentiation. Here, a 'stem cell' miRNA cluster clearly shows up in red, which highlights the overexpressed miR-NAs in the pluripotent stem cells. Most of these miR-NAs have already been hypothesized to cooperatively regulate pluripotency  (25). The group comprises miR-17/20/93/106/302/. /519/520 with shifted seed sequences (AAAGUG, AAGUGC, AGUGCU), and miR-411 with seed sequence AGUAGA. Figure  3B  represents a functional screening dataset where we measured the relative levels of breast cancer stem cells (bCSCs) in a human breast adenocarcinoma cell line (SUM159 cells) upon systematic and individual overexpression of miRNAs  (21).
The squares on the full network below correspond to the interesting areas. MiRNAs with low expression are set as semi-transparent. A few small clusters of miRNAs with high differential expression are highlighted (blue squares): Clusters 1 and 2 correspond to miR-29 family located on chromosomes 1 and 7, and cluster 3 correspond to miR-503-5p/424-5p located on chromosome X. The two major clusters in green and red squares (i. e. Xq27, 14q32) of 95 and 197 kilobases, respectively, show groups of miRNAs associated with good and poor prognosis, respectively. higher) bCSC proportions. To determine the efficiency of miRViz to compare different datasets and the possibility that it can raise biological questions of interest, we can compare Figure  3A  and B. Here, the miRViz representation highlights the redundant action of the 'miRNA stem cell' cluster on the balance of the bCSC phenotype.
Which network for which dataset, and further validations An important question is: which network should be used for a given dataset? The simple answer is 'all'. We suggest to use all the networks, starting with small ones (Seed2 7, Di-ana50 and Genomic Distance 50k clusters 3+ for human). We suggest then to visually identify enrichment of highly connected groups of miRNAs. In our experience, we often find enrichment in the Seed2 7 and/or Genomic Distance networks. Yet, the most appropriate network to explore a specific dataset depends on the biological question to answer. When focusing on mRNA targets repressed by miR-NAs, the Seed2 7 (and in a second time the co-regulation networks: Diana50, DianaTarBase50 and TargetScan54) should be used. When trying to identify polycistrons, Genomic Distance 2k shows co-expressed miRNAs in a given neighborhood on the genome.
The 50k version is more dedicated for large genomic reorganizations, as found in cancer. To note, even if miRViz is particularly useful for raw expression and differential expression datasets, any large miRNA dataset with numerical scores can benefit from miRViz. Another important question is: what to do with miRViz results? First, the mapping itself is interesting, and miRViz provides an export function to show a given enrichment in a figure. Second, it guides the validation steps. When investigating the phenotypic role of a given miRNA, a practical experiment consists in knocking-down (or over expressing) this miRNA and measure the phenotypic outcome. If many miRNAs of a given family are co-expressed, modulating one miRNA out of the whole family may lead to no or minimal phenotypic effect, as the other miRNAs from the family may still repress the mRNA targets.
CONCLUSION We propose that the webserver application miRViz can be used to visualize numerical miRNA datasets. We have illustrated the results that can be obtained for miRViz through various examples, including miRNA expression and functional screening datasets. For miRNA profiling, the network-based visualization proposed here provides clear ways to present datasets that are complementary to volcano plots for expression data. In particular, the Seed2 7 network allows rapid identification of hit miRNA families, and quickly identifies miRNA redundancies.
ACKNOWLEDGEMENTS We thank  C. Cochet  and  J. J. Feige  for helpful discussion, and all external testers of miRViz. We thank  Christopher Berrie  for scientific English editing. This research was funded by the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA, DRF/IRIG) , the  University Grenoble Alpes (UGA).