dataesr/scientific-paragraphs-categorization huggingface dataset:
Structural prediction of mutant dimers supports dimerization data To establish a structural basis of the in vitro dimerization results, we predicted minimal free energy models of the wild type and mutant MMTV RNA dimers using RNAstructure. This software predicted that wild type RNA dimerizes via the pal II sequence (Figure  4A ), while deletion of pal II (SA042) induces dimerization via the PBS pal (Figure  4B ), in agreement with our RNA dimerization and SHAPE data (Figure  3 , Figure  5 , Figure  6 , and Figure  7 ). The predicted structure of SA051 (deletion of 11 nt pal sequence within PBS), revealed that the native structure (like wild type homodimer) was restored except for the loss of the PBS loop  (Figure  4C ) which could in part explain its ability to dimerize though much less efficiently when compared to the wild type (Figure  3B  and Figure  3C ).
Structural analyses The secondary structure of the 5' region of the MMTV gRNA containing either 120 nt of gag (432 nt of the 5' end from R) or 400 nt of gag (containing 712 nt of the 5' end from R) were predicted using the Mfold server  [67, 68]. The SHAPE reactivity data of the first 432 nt region was applied in RNAstructure software (version 5. 3)  [69]  as constraints to develop the final structural model. The Mfold-predicted and SHAPE-validated structures were redrawn with the XRNA software (http://rna. ucsc. edu/rnacenter/xrna/xrna. html). MMTV nucleotide positions refer to HYBMTV a molecular clone created by Shackleford and Varmus  [70].
Phylogenetic analyses Nucleotides 1-432 of the gRNA of different strains of MMTV were aligned using Clustal Omega  [71, 72]  and a consensus or "conservation annotation" structure for these strains was predicted using RNAalifold server  [73, 74]. The accession numbers for different MMTV strains used for sequence alignment are AF228550. 1 (mtv-1)  [75] , L37517. 1 (mtv-6)  [76] , AF033807. 1  [77] , M15122. 1 (BR6)  [78] , D16249. 1 (JYG)  [78] , AF228551. 1 (C3H/HeJ)  [75] , X00018. 1 (GR)  [79]  and AF228552. 1 (C3H)  [75].
In vitro dimerization assay In vitro dimerization assays of wild type and mutant MMTV RNAs were performed as described previously  [21, 82]. Briefly, 300 nM RNAs were heated at 90°C and incubated in ice for 2 min followed by a 37°C incubation for 30 min in either the dimer (final concentrations: 50 mM sodium cacodylate, pH 7. 5; 300 mM KCl; 5 mM MgCl 2 ) or monomer (final concentrations: 50 mM sodium cacodylate, pH 7. 5; 40 mM KCl; 0. 1 mM MgCl 2 ) buffer. The samples were divided equally and analysed by electrophoresis in TBM (50 mM Tris base, 45 mM boric acid, 0. 1 mM MgCl 2 ) and TB (50 mM Tris base, 45 mM boric acid) ethidium bromide prestained agarose gels at 4°C or 20°C, respectively. Density of the dimeric and monomeric species was quantitated using Quantity One software, and percentage of dimerization was calculated and expressed as dimerization relative to the wild type for each mutant, as described previously  [21].
Statistical analysis The standard paired, two-tailed Student's t-test was performed between the wild type and mutant clones to determine statistically significant differences. For in vitro dimerization assays, and in vivo packaging and propagation studies, a p value of ≤0. 05 was considered significant. Additional file 2: Consensus RNA secondary structure prediction of MMTV packaging signal RNA using sequences from eight different strains. Accession numbers of MMTV strains are the same as mentioned in materials and methods section. The sequences of the 8 MMTV strains were aligned using Clustal Omega and the aligned sequences were used as input in the RNAalifold server  [73, 74]  to predict the consensus or "conservation annotation" structure. The number/color scale on the x-axis indicates base pairing conservation with 1 (red color) representing completely conserved base pairing to 6 (violet color) representing 6 different types of base pairings at that position among the various strains (see Methods for details).
Phylogenetic conservation of the sequences of major structural motifs of MMTV packaging signal RNA in different strains. (A) Clustal Omega sequence alignment of eight different strains of MMTV packaging signal RNA. Sequences of the major structural motifs are highlighted in different colors, whereas the 11 nt pal sequence within PBS is boxed. The accession numbers for the MMTV strains are provided in the Materials and Methods. (B) Sequence conservation superimposed on the SHAPE validated structure. The nucleotides are color-coded based on their conservation level, ranging from <75 to 100%.
Acknowledgments This research was funded primarily by grants from  Terry Fox Fund for Cancer Research  ( 2009-06 ) and the  United Arab Emirates University  ( UAEU-NRF 26 ) and, in part, by a grant from the  College of Medicine and Health Sciences  ( NP NP/10/22 ) to TAR. RM is supported by the  Centre National de la Recherche Scientifique  (C. N. R. S. ). SJA was supported through a scholarship from the  Office of the Deputy Vice Chancellor for Research and Graduate Studies at the United Arab Emirates University (UAEU).
Additional file 1 : 1 Phylogenetic conservation of the sequences of major structural motifs of MMTV packaging signal RNA in different strains. (A) Clustal Omega sequence alignment of eight different strains of MMTV packaging signal RNA. Sequences of the major structural motifs are highlighted in different colors, whereas the 11 nt pal sequence within PBS is boxed. The accession numbers for the MMTV strains are provided in the Materials and Methods. (B) Sequence conservation superimposed on the SHAPE validated structure. The nucleotides are color-coded based on their conservation level, ranging from <75 to 100%.
From our experience (see  Braud et al. , 2014a) , the 4-year duration of the experiment and the large area involved in the monitoring, ensures that significant events will be captured within the duration of the monitoring in at least one of the small catchments. Long-term time series from operational networks are also collected and analysed to get information about hydrological processes over longer timescales. Finally, an innovative monitoring strategy for flash floods, relying on recent progress in instrumentation and sensors, is proposed, complemented by opportunistic measurements to document discharges and soil moisture conditions during floods, as well as to perform geochemistry sampling to trace back water origin. Data analysis and models are combined in an iterative way (Fig. 2 ) to increase our process understanding and modelling capability.
, 2014b). However, over the 4-year project duration, these probabilities increase to ≈ 0. 9 and ≈ 0. 5 respectively. The likelihood of recording precipitation values above 150 mm is therefore quite high over the project duration, which gives hope that flood events will also be monitored on the three small catchments. In the following, we successively describe, for the various scales, the scientific questions addressed by the experiments (see also Fig. 1 ) and the experimental set-up itself. Opportunistic data collection during floods is also described.
5 m resolution satellite images (Quickbird and/or Pléiades images), leading to a hillslope typology relating soil moisture dynamics, infiltration capacity, soil hydraulic properties, soil structure and vegetation to more easily measurable quantities  (Morschel, 2011). The sub-surface flow field experiments, as well as the electrical resistivity surveys, are analysed to understand water pathways within the topsoil and the underlying altered bedrock in order to derive lateral flow velocity and test the relevance of the "fill and spill" mechanism. If possible, the altered bedrock storage capacity will be assessed. All the data acquired on the hillslope scale can be used to run detailed models of hillslopes with different hypotheses about active processes (e. g. Troch et al. , 2003; Weiler and McDonnell, 2004, 2007; Graham and McDonnell, 2010)  in order to verify the consistency between the observed and simulated water pathways and fluxes.
In order to obtain high-resolution rainfall, relevant for the interpretation of the hydrological response to flash floods on a small scale (e. g. Creutin and Borga, 2003) , research radars were deployed during the autumn in 2012 and 2013 (and will hopefully be deployed in the autumn of 2014) (see  Ducrocq et al. (2014)  for details), in combination with high-resolution rain gauge networks such as the HPiconet (see Fig. 5 ). The data will be used to set up and assess distributed hydrological models, focusing mainly on lateral flow representation and network connection (see Sect. 3. 2). The models will be used in a hypothesis testing framework  (Clark et al. , 2011; Fenicia et al. , 2011)  in an iterative way as shown in Fig. 2 , allowing a better understanding of active processes, in particular an assessment of the relative importance of rainfall and landscape spatial variability.
A more detailed reanalysis will be performed for the HyMeX EOP (2012-2015) using the additional rain gauges and research radars, with a finer grid (100 m) and time (15 min) resolutions. For the quantification of rainfall uncertainty, two approaches are considered. The first one relies on a statistical analysis of rainfall errors 1. using rain gauge data to establish reference rain amounts for the radar-only estimates  (Kirstetter et al. , 2010; Delrieu et al. , 2014a)  and 2. through a novel approach exploiting the Kriging estimation variances for the rain gauge and radar-rain-gauge merging estimates. The radar errors, analysed conditionally with respect to the rain intensity thanks to generalized additive models for www. hydrol-earth-syst-sci. net/18/3733/2014/ Hydrol. Earth Syst. Sci. , 18, 3733-3761, 2014 location, scale and shape (GAMLSS), are shown to be radarrange-and rainfall-type-dependent  (Delrieu et al.
Stream gauging during floods When an important event is forecasted several teams of two people are sent to the field in order to gauge flooding rivers at pre-selected sections (the operational and research stations in Fig. 3 ; gauging is always undertaken from a bridge). For the largest sections, the teams are equipped with surface velocimetry radars (SVR) (Fig. 8a ), which are used to measure the surface flow velocity, pointing upstream (Fig. 8b ) at 13 positions across the section (see Fig. 8c ). The water level must also be measured at the time of the measurement. The technique allows measuring surface flow velocities safely and rapidly. Combined with pre-and/or post-event bed geometry surveys, it yields useful stream gauging data for sections not documented otherwise or for discharge values which cannot be measured with standard intrusive methods  (Dramais et al.
Infiltration and water redistribution are modelled using an efficient solution of the Richards equation  (Ross, 2003; Varado et al. , 2006b)  with hydraulic properties described using standard pedo-transfer functions  (Rawls and Brakensiek, 1985). The model takes into account the vertical heterogeneity of soil hydraulic properties as described in the available soil databases. Excess runoff is instantaneously directed towards the closest river reach where water flow is modelled using the kinematic wave equation. Evapotranspiration components have also been added in order to provide continuous simulations  (Vannier, 2013). The model will be enriched step by step (e. g. Fenicia et al. , 2008)  to test the following hypotheses: 1. Does the improved description of soil hydraulic properties, as derived from in situ observations, improve the realism of model simulations? 2.
This first result must be refined by: 1. accounting for the soil water isotopic or chemical composition, which is now monitored before the beginning of each event; 2. applying a three-component mixing, including rainfall water, soil water and deep water; 3. considering a larger range of events to relate the contribution to the magnitude of the floods; 4. relating both the old and new water proportions to the understanding of the real processes of both surface and sub-surface flow. This will be achieved by combining geochemical data with physically based hydrodynamical models. Note also that it is expected that a flood of major magnitude would bring different contributions in surface or sub-surface water flows at the outlet of the catchment. This information will be used for calibrating the hydrological processes with multi-variable controls, such as discharges, of course, but also geochemistry.
What information about dominant, regional-scale processes can be derived from the combination of data analysis and modelling? This section illustrates how the iterative approach of Fig. 2 , combining observation and modelling, and the bottom-up and top-down approaches are used to enhance our knowledge of dominant active hydrological processes in the study area. In both cases, we use discharge recession analysis from the historical records, which can provide useful information about catchment characteristics or functioning (see the recent review of  Troch et al. , 2013). The iterative approach is illustrated using the CVN noncalibrated model, described in Sect. 3. First simulations, based on soil storage capacity derived from available soil databases, which only describe the topsoil relevant for agronomic purposes, lead to poor simulation results, as illustrated in Fig.
17  for the Ardèche at the Meyras catchment (no. 3 in Fig. 14a ). The model simulation is too responsive, with an overestimation of peak discharges and recessions that are too quick. The specification of the water storage capacity of the soil is therefore re-examined, using new data analysis (recession analysis) of available long-term discharge series. The objective is to estimate catchment storage capacity and saturated hydraulic conductivity in the weathered bedrock, which are not documented in the existing soil databases. Weathered bedrock has been shown to be a significant source of water storage within the studied catchments  (Vannier et al. , 2013). Geology is identified as the main driver governing the range of these characteristics  (Vannier et al. , 2013). Figure  17  shows that the use of this information into the CVN model improves both the long-term and event discharge simulation, even if the peaks are still overestimated and the recessions are still too quick.
Acknowledgements. The  FloodScale  project is funded by the  French National Research Agency (ANR)  under contract no. ANR 2011 BS56 027 , which contributes to the  HyMeX program. It also benefits from funding by the  MISTRALS/HyMeX program  (http://www. mistrals-home. org). The OHM-CV is an observation service certified in 2006 and funded by the  Institut National des Sciences de l'Univers/Surface et Interfaces Continentales. The Ph. D. theses of  M. Adamovic  and  A. Wijbrans  are funded by  Region Rhône-Alpes. The authors acknowledge  CNES  for providing the Pléiades images and data processing through a  TOSCA program  project,  Météo-France  for providing rainfall data, SCHAPI for providing discharge data and  Alexandre Hauet  for his help in getting the EDF-DTG discharge data. Sandra Perez  from  UMR ESPACE  contributed to the building of the  FloodScale  project.
The authors thank  Stanislas Bonnet ,  Louise Jeandet ,  Mickaël Lagouy ,  Florent Le Floch  and  Mélissa Vuarant  for their participation in the field infiltration tests and deployment of the saturometer in the Claduègne catchment;  Martin Caliano  for his help in the SOP1 field survey,  Isabella Zin  and  Jeremy Chardon  for providing the analog rainfall forecasting and  Gilles Molinié  for his investment in the HPiconet network. The additional radars deployed during SOPs 2012 and 2013 were provided by  EPFL, LaMP ,  NSSL/NOAA  and  NOA. The HyMeX database teams (ESPRI/IPSL and  SEDOO/Observatoire Midi-Pyrénées ) helped in accessing the data. We also thank  D. Archer  and one anonymous reviewer for their valuable comments, which helped improve the quality of the paper. Edited by:  H. Cloke.
3, 0. 5, 0. 6, 0. 9, 3. 9 km 2 1, 2. 5, 10 km 2 10, 21, 60 km 2 3 (Gazel), 43 (Claduègne), stations 116 (Auzon) km 2 Limnimeter 5 limnimeters, 18 thermo-buttons None None 11 limnimeters (7 mini-Diver network and survey of gullies during and 4 CTD-Divers) in and after events in the Claduègne catchment the 0. 3 km 2 sub-catchment Geochemistry Sampling of rainfall, Continuous temperature None Continuous temperature and soil and groundwater and conductivity conductivity (Gazel, Claduègne) during events and continuous at the outlet and four limnimeters with electrical conductivity at the outlet conductivity and temperature Infiltration Performed during Collection of existing data Collection of 17 sampled fields using tests hillslope monitoring existing data infiltrometers and Beerkan DEM 1 m lidar DEM 1 m lidar DEM 1 m lidar DEM 1 m lidar DEM (Gazel, Claduègne), 25 m DEM (Auzon) Pedology Languedoc-Roussillon soil Languedoc-Roussillon Languedoc-Roussillon Ardèche soil database database and landscape soil database soil database segments locally.
Viral microRNAs (miRNAs) regulate gene expression of viral and/or host genes to benefit the virus. Hence, miRNAs play a key role in host-virus interactions and pathogenesis of viral diseases. Lately, miRNAs have also shown potential as important targets for the development of novel antiviral therapeutics. Although several miRNA and their target repositories are available for human and other organisms in literature, but a dedicated resource on viral miRNAs and their targets are lacking. Therefore, we have developed a comprehensive viral miRNA resource harboring information of 9133 entries in three subdatabases. This includes 1308 experimentally validated miRNA sequences with their isomiRs encoded by 44 viruses in viral miRNA 'VIRmiRNA' and 7283 of their target genes in 'VIRmiRtar'. Additionally, there is information of 542 antiviral miRNAs encoded by the host against 24 viruses in antiviral miRNA 'AVIRmir'.
Miravirsen is the first miRNA-targeted drug to enter in the Phase IIa clinical trial with antiviral activity against HCV  (24). Therefore, it would be important to have resources on viral miRNA that can prove helpful in developing antiviral therapeutics. There are many repositories for experimental and predicted miRNAs; their cognate targets and associated interactions like miRBase  (33) , Plant miRNA database (PMRD)  (34) , microRNA. org (35) , TarBase  (36) , miRTarBase  (37) , mirDB  (38) , miRWalk  (39) , miRecords (40) etc. , which encompass detailed information about the miRNAs found in different organisms. There are some repositories of predicted viral miRNAs and their targets like VirMirDb  (41) , vhotdb  (42) , miRNEST  (43) , ViTa  (44)  and RepTar (45) but a dedicated experimental viral miRNA archive along with their targets is lacking.
Database organization For lucid presentation, viral miRNA (VIRmiRNA) resource is organized into three different categories, namely VIRmiRNA, VIRmiRtar and AVIRmir to furnish experimentally validated information of viral miRNAs, their targets and antiviral miRNAs respectively. Broadly each module provides information regarding the following fields: (i) miRNA ID, (ii) virus, (iii) taxonomy, (iv) nomenclature, (v) miRNA sequence, (vi) length, (vii) GC content, (viii) pre-miRNA, (ix) arm, (x) cell line, (xi) experimental method and (xii) references. Additionally in VIRmiRNA sub database, outputs of the basic local alignment search tool (BLAST)/Alignment of the seed and complete miRNA sequence with that of viral as well as cellular miRNAs are provided. Predicted secondary structure of viral miRNA is also incorporated in individual entry.
In VIRmiRtar and AVIRmiR sub databases, important fields like target gene name, miRNA-binding region, i. e. untranslated region or coding sequences and seed position are also given wherever available. The database is hyperlinked to other useful resources like UniProt, GO, GenBank, PubMed and Taxonomy browser. Also, other important miRNA related resources on the web are listed separately on the 'Related Links' page.
Database statistics VIRmiRNA resource encompasses experimentally validated information of 9133 entries divided into three subarchives, namely VIRmiRNA, VIRmiRtar and AVIRmiR. Detailed statistics of each of the module is depicted in Figure  2. VIRmiRNA subdatabase has information of 1308 viral miRNA including 538 isomiRs sequences that belong to 44 viruses infecting a range of host organisms, namely human (17 viruses), other mammals (16 viruses), birds (7 viruses), invertebrates (3 viruses) and plants (1 virus). It includes a maximum of 287 miRNAs of Pseudorabies virus (PRV) followed by 111 miRNAs of Rhesus lymphocryptovirus (RLCV) and miRNAs of other important viruses like EBV, Herpes simplex virus (HSV) and Duck enteritis virus (DEV) etc. (Supplementary Table  S1 ). These miRNAs have been validated on different cell lines like IB-RS-2, PK-15, HEK, BC, chicken embryo fibroblasts (CEF), Vero cells etc.
(Figure  2e ). It has been reported earlier that some genes were being targeted by more than one miRNA  (51, 52). Similar trend was observed for viral miRNA targets like CCNT2, BRWD1, BTBD3, FNDC3A, LCOR etc. that were under the regulation of multiple miRNAs as briefed in Table  1  and detailed in Supplementary Table  S4. The third submodule, i. e. AVIRmiR deals with small yet quite important data about antiviral miRNAs. It contains 542 antiviral miRNAs acting against 26 different viruses (Supplementary Table  S3 ). These miRNAs were predominantly tested using HuH-7, HEK, A549, NIH-3T3 and HepG2 cell lines (Figure  2c ). These miRNAs have been mostly validated using RT-PCR, luciferase reporter assay, microarray and northern blot (Figure  2f ). It has been described earlier that a miRNA can target numerous genes  (51).
Database search In the search menu option, user can select any of the three submodules to search for a given keyword in all or specific fields. The search output provides information on the various aspects of miRNAs, i. e. nomenclature, sequence, length, GC content, cell line, experimental method and reference. Additionally, internal links showing the output of tools like Seed BLAST/Align (8mer/7mer) of miRNA seed sequence with that of viral miRNA (VIRmiRNA) and cellular ones (miRBase) is displayed. In search output, we have also provided sorting and filtering functionality. Also, we have made the search results interactive by interlinking a viral miRNA entry with its experimental/predicted targets. By clicking on the miRNA ID, user can get complete details of that record. Result of the search output for KSHV miRNA is shown in the Figure  3.
Advanced search Advanced search using logical operators helps the user to find the appropriate data stringently in each of the subdatabases. User can successively build the query by entering the desired keyword and selecting the corresponding field and logical operators (AND/OR). For example in VIRmiRNA, on searching EBV in the 'Virus' field, the output displays 52 entries, then by selecting 'Method' field as Northern blot, the output shows 46 entries. Further by choosing 'Length' field as 22, the final result output lists 26 entries.
Database browsing VIRmiRNA browse gives a list of viruses along with the number of miRNAs for that virus, e. g. HSV-1 has 48 miRNAs. On clicking a virus name, information of all the miRNAs encoded by that virus will be displayed, and further details can be viewed by clicking on its ID. In order to know the classification of the virus, we have also provided an external link to 'NCBI Taxonomy browser' for each virus. VIRmiRtar browse provides a list of viral miRNAs along with the number of their target genes. For example, ebv-miR-bart19-3p has maximum of 370 targets, which can be viewed by clicking on the particular miRNA. AVIRmiR browse displays a list of viruses along with the number of antiviral miRNAs and virus taxonomy.