dataesr/scientific-paragraphs-categorization huggingface dataset:
Data The non-redundant RNA database maintained on RNA3DHub (23) on 9 September 2016, version 2. 92, was used. It contains 845 all-atom molecular complexes with a resolution of at most 3 Å. From these complexes, we retrieved all RNA chains also marked as non-redundant by RNA3DHub. Each chain was annotated by FR3D. Because FR3D cannot analyze modified nucleotides or those with missing atoms, our present method does not include them either. If several models exist for a same chain, the first one only was considered. For the rest of this paper, the base pairs extracted from the FR3D annotations are those defined in the Leontis-Westhof geometric classification  (24). They are any combination of the orientation cis (c) (resp. trans (t)) with the name of the side which interacts for each of the two nucleotides: Watson-Crick (W) cis • (or for trans), Hoogsteen (H) (or ) or Sugar-Edge (S) (resp.
Secondary structure For each chain a secondary structure without pseudoknots was deduced from the annotated interactions, as follows. First all canonical Watson-Crick and wobble base pairs (i. e. A-U, G-C and G-U) were identified. Then, since many structures are naturally pseudoknotted, we used the K2N (25) implementation in the PyCogent (26) Python module to remove pseudoknots. Problems arise when a nucleotide is involved in several Watson-Crick base pairs (which is geometrically not feasible), probably due to an error of the automatic annotation. Those discrepancies were removed with a ad hoc algorithm such that if a nucleotide is involved in several Watson-Crick base pairs, we remove the base pair which belongs to the shortest helix.
The RINs can be accessed and browsed from two different perspectives. The first one is the catalog, a list of all the RINs (which can be restrained to distant SSE RINs or adjacent SSE RINs by the user). The second one is a graph which represents the network of RINs: a RIN r 1 is linked to a RIN r 2 if r 1 is included in r 2 and there is no other RIN which includes r 1 and is included in r 2. This network of RINs can also be restrained to distant SSEs RINs or adjacent SSEs RINs by the user. In both views, pictures representing the RINs are clickable and open the RIN specific page. Older versions of the database are kept indexed and accessible. At the present time, the version of RNA3DHub 2. 92 is available.
RIN search by interaction features. The large amount of RINs makes the exploration of the results difficult. To ease this process we offer a filter by type of interactions. A minimal or maximal amount of any type of edge--long-range or not, combined with the Leontis-Westhof classification--can be chosen. A catalog with only those RINs fulfilling the ensemble of constraints is then built. RIN identification in novel structures. As an additional utility, we provide an automatic pipeline in which a structure file, in the mmCIF format, can be uploaded with the name of a specific chain it contains. The structure is annotated by FR3D and all RINs found are extracted. An additional parameter allows to consider, or not, the annotations marked as 'near' by FR3D. (We remind the reader that 'near' interactions are never considered for identifying the RINs in the CaRNAval database.
The full graph of recurrent interaction networks The 845 structures extracted from the PDB contain 912 RNA chains identified as non-redundant. From those all 1426 pairs of SSEs having FR3D annotated interactions between them were identified, belonging to 165 chains. In total 337 RINs were identified, corresponding to 6056 occurrences inside the non-redundant dataset. This number contains duplicate locations: if a RIN has as subgraph another interaction networks, both are counted. By connecting two RINs if one is a subgraph of the other, a graph can be drawn. The complete graph of RINs of direct inclusions can be visualized at http://carnaval. lri. fr. This graph is constituted of 28 connected components. Among them, 25 components are of small size: from 1 to 9 RINs each. The three other components are much larger and are discussed in detail below.
DISCUSSION In this work, we present a fully automated method for extracting and classifying RNA substructures based on their interactions rather than sequence or context. Through a rigorous mathematical description of the RNA interactions, making a distinction of those within an SSE (local) and those between two SSEs (long-range), our automatic ab initio method detects all RINs between two structure elements. The collection of all RINs is presented in a database called CaRNAval, freely accessible at http://carnaval. lri. fr. The principal novelty and key element of our methodology is to cluster motifs solely on the base of the similarity of their interaction networks, regardless of the nu-cleotide composition. This approach enables us to demonstrate the extraordinary versatility and diversity of the well known A-minor contacts, where an unsuspected variety of sequences fold into the exact same intricate network of interactions.
ACKNOWLEDGEMENTS We thank  Mahassine Djelloul ,  Alexis Lamiable ,  Alexis Delabrière  for their help at a very early stage of the work;  Yann Ponty  for the RIN drawing software;  Anton Petrov  for the 3D alignment tool;  Neocles Leontis  for fruitful discussions; and  Laurent Darré  for technical help.
FUNDING Natural Sciences and  Engineering Research Council of Canada  [ RGPIN-2015-03786 ,  RGPAS 477873-15 ];  Genome Canada  [BCB 2015];  Canadian Institutes of Health Research  [ BOP-149429 ];  Fonds de recherche du Québec  [ 211485 ,  FQ-175959 ];  Erasmus Mundus ,  Azrieli  and  Fonds de recherche du Québec Postdoctoral Fellowship  (to V. R. );  French National Research Agency  grant [ ANR-15-CE11-0021-01 ] and  Labex  [ ANR-10-LABX-0036  NETRNA] (to E. W);  French Fondation pour la Recherche Médicale  [FRM DBI20141423337] (to J. W. and A. D. ). Funding for open access charge:  French National Research Agency.
23, 24 Of note, clinical trials have evaluated the safety, efficacy, and tolerability of ncRNAs in the treatment of various diseases, such as chronic hepatitis C (www. clinicaltrials. gov, NCT01200420) and kidney disease (www. clinical trials. gov, NCT02855268). Excellent publications have recently reviewed the role of ncRNAs as potential therapeutic targets in CVD,  18, 19, [25] [26] [27]  and therefore this issue is beyond the scope of this review. Since the great majority of investigations are in pre-clinical stages, further efforts are needed to demonstrate the clinical safety and efficacy of ncRNAs in cardiovascular therapy.
Circulating microRNAs in biomarker-guided cardiovascular therapy miRNAs are a group of small ncRNAs (19-25 nucleotides in length) that regulate gene expression at the post-transcriptional level. Approximately 2500 human miRNAs have been annotated in the miRBase database (http://www. mirbase. org). 39 miRNAs are Figure  2  Coding and non-coding transcriptome. Although approximately 80% of the human genome is transcribed, only 1-3% of this corresponds to protein-coding nucleotide sequences (messenger RNAs, green circles). The remaining 97-99% corresponds to RNAs not translated into proteins: non-coding RNAs (orange), except for some specific exceptions. The non-coding RNA transcriptome includes: circular RNA (circRNAs), enhancer RNAs (eRNAs), germline small RNAs (gsRNAs), long non-coding RNAs (lncRNAs), microRNAs (miRNA), piwi-interacting RNAs (piRNAs), ribosomal RNAs (rRNAs), ribozymes, small conditional RNAs (scRNAs), small interfering RNAs (siRNAs), small nuclear RNAs (snRNAs), small nucleolar RNAs (snoRNAs), transfer RNAs (tRNAs), and Y RNA (YRNA).
The aim of the action is to coordinate the effort of harmonizing the snow monitoring practices, by promoting new observing strategies, bringing together different Sensors 2018, 18, 2016 3 of 51 communities, facilitating data transfer, upgrading and enlarging knowledge through networking, exchange and training, and linking them to activities in international agencies and global networks. One of the first activities of Harmosnow was to carry out a survey to obtain an updated picture of the existing variety of snow measurement practices and instrumentations in use by the European institutions. The survey collected a compilation of the measured snow properties and associated measurement techniques that are applied by the participating European countries for a large variety of applications. This paper aims to summarize the results of this survey, providing an overview on the European snow measurements that are carried out for a large variety of applications.
g. , soil, rocks, shrubs, etc. ). By using appropriate retrieval algorithms, these variables can be hence derived remotely (e. g. ,  [19] [20] [21] [22] ). Note that the ground-based instruments that are used to measure snow electromagnetic properties are similar (as frequencies, polarizations, etc. ) to those that are installed on board satellites, and that these can be used to calibrate and validate satellite-derived snow data and extend the analysis over a broader area. Solid precipitation has a variety of forms, including snowflakes, snow crystals, and graupel (see definitions in the Global Cryosphere Watch site: http://globalcryospherewatch. org/about/ about_solidprecip. html). The amount of precipitation is an essential input for all of the models that simulate the snow cover and produce forecasts of weather, avalanches, melt water amount, and floods.
As for the other snow properties, most of the respondents measure more than one electromagnetic parameter. For instance, among the respondents who apply spectro-radiometers to measure spectral albedo (from Finland, France, Norway, and Switzerland), four derive the broadband albedo from the same instrument/dataset. In the microwave spectral region, the snow backscatter and snow brightness temperature are measured through ground-pointing radars and microwave radiometers by ten and eight respondents, respectively; only the backscattering coefficient is measured by the University of Pavia (Italy), the Norwegian Water Resource and Energy Directorate (Norway), the Swedish Meteorological and Hydrological Institute (Sweden), and University of Zurich (Switzerland), and only the brightness temperature is measured by the Grenoble-Alps University (France), the University of Oslo (Norway), the Middle East Technical University (Turkey), and Met Office (the U.
K. ), while the both of them are measured at the Finnish Meteorological Institute (Finland); Centre d'etudes de la neige, Meteo-France/CNRS (France); As for the other snow properties, most of the respondents measure more than one electromagnetic parameter. For instance, among the respondents who apply spectro-radiometers to measure spectral albedo (from Finland, France, Norway, and Switzerland), four derive the broadband albedo from the same instrument/dataset. In the microwave spectral region, the snow backscatter and snow brightness temperature are measured through ground-pointing radars and microwave radiometers by ten and eight respondents, respectively; only the backscattering coefficient is measured by the University of Pavia (Italy), the Norwegian Water Resource and Energy Directorate (Norway), the Swedish Meteorological and Hydrological Institute (Sweden), and University of Zurich (Switzerland), and only the brightness temperature is measured by the Grenoble-Alps University (France), the University of Oslo (Norway), the Middle East Technical University (Turkey), and Met Office (the U.
This reporting practice leads to a bias in the HS data records, which is an issue for the data assimilation applications. This issue was addressed by the WMO 69th Executive council in May 2017, to improve operational exchange of snow data and reporting practices. Our analysis showed that most of the respondents that apply the manual method also apply automatic techniques. Important drivers for the automatization of the snow macrophysical properties are the snow and avalanches services in mountain areas. In order to be able to provide the public with timely information about the snow situation and the avalanche hazard, automatic stations with a variety of reliable meteorological and nivological sensors are needed. These data are often used to drive snow models, which provide even more information in high temporal resolution.
Acknowledgments: We thank all of the participants of the  COST Action   ES1404  and especially  Charles Fierz ,  Anna Haberkorn ,  Pavla Dagsson Waldhauserová , and  Outi Meinander , for their support and fruitful comments to the results. We also thank all of the respondents to the survey who provided the material for this study. We are grateful to  Sylvain Joffre , who had a key role in planning and initiating the  COST Action   ES1404.
IIR data acquisition The IIR acquisition timing is organized around successive cycles described in Table  1. Each cycle is composed of five sequences. Each of these five sequences includes three successive calibration images -one per channel, followed by three successive Earth view images in each channel. In each cycle, the calibration images are blackbody (BB) views in the first sequence, and are deep space (DS) views in the other four sequences. The total duration of a cycle is 40. 92 s, i. e. 8. 184 s per sequence. The elapsed time between two successive Earth views in a given channel is 8. 184s, during which the satellite has moved forward by about 55 km, so that two successive Earth view images always overlap. The duration of a full orbit corresponds to about 145 cycles. In this paper, IIR acquisition cycles are counted from cycle #0 defined as the first cycle after night-to-day transition.
Motivation for a change The need to improve the Version 1 IIR Level 1b data product was motivated by two different types of issues, independently highlighted after the analysis of numerous years of Version 1 data. The first issue was the striping effect, which was detected through the visual inspection of IIR browse images. The second issue, the presence of biases at 30° N-60° N, was made evident after careful statistical comparisons with MODIS/Aqua. Even though these two issues seemed unrelated at first glance, this study demonstrates that they are not and that both can be corrected simultaneously.
Seasonal IIR-MODIS night/day differences at 30° N-60° N Assessing the IIR radiances required external comparison with other instruments. For this purpose, comparisons with MODIS/Aqua have been carried out since launch by following two complementary methods: (i) the "relative" instrument-toinstrument inter-comparison approach, and (ii) the "stand alone" simulations-to-observations comparison approach  (Scott, 2009; G17). For both approaches, IIR channels IIR1, IIR2, and IIR3 were paired with Collection 5 (C5) MODIS/Aqua channels 29, 31, and 32, respectively. Collocated IIR and MODIS observations are from the "REMAP'' product available at the AERIS/ICARE Data and Services Center (http://www. icare. univ-lille1. fr). IIR and MODIS pixel sizes are similar (1 km 2 ) and IIR and MODIS/Aqua are both in the A-Train, so a lot of collocated quasi-simultaneous observations are available, with MODIS viewing angles for pixels collocated with IIR decreasing from 20° at the equator to a few degrees near the poles.
2 K for IIR1 and 0. 4 K for IIR2 and IIR3. These inter-comparisons have been further assessed using the stand-alone approach through time series since launch of [simulated-observed] BTDs, also called residuals, for each IIR and MODIS channel in clear sky conditions over oceans (G17). The simulations were performed using the 4A/OP radiative transfer model and the Gestion et Étude des Informations Spectroscopiques Atmosphériques (GEISA) spectroscopic database (http://ara. lmd. polytechnique. fr), with atmospheric and surface inputs from 3h/5km collocated ERA-Interim products  (Dee et al. , 2011). The clear sky mask was based on co-aligned observations from CALIOP and IIR, further extended to the IIR 69km swath. The stand-alone approach showed that the slow decrease over time of both nighttime and daytime IIR1-MODIS29 BTDs as seen in Fig.
Comparisons with MODIS along the CALIOP track After the calibration biases demonstrated through comparisons with MODIS paired channels using the relative approach, the comparisons with MODIS were refined in order to integrate information related to the IIR internal calibration. As in the relative approach, the pairs of IIR and MODIS channels considered for the comparisons are IIR1-MODIS29, IIR2-MODIS31, and IIR3-MODIS32. MODIS data is from C5, but using MODIS Collection 6  (Toller et al. , 2013)  would not change the discussion (not shown). For simplicity, the calibrated radiances are assessed now using only the track pixels. Cloud-free conditions determined using the CALIOP 5-km cloud layer products  (Vaughan et al. , 2017)  are selected to favor homogeneous scenes and facilitate the reasoning.
The uncooled micro-bolometer used in the IIR instrument was the first of its kind to be used for radiometric analysis. It appeared to be fully able to meet the radiometric performance required for atmospheric observations. The IIR Level 1b products have been reprocessed by the CALIPSO project at the NASA Langley Research Center (LaRC) using the calibration corrections described in this paper. The corrections are functions of the IIR cycle number for any orbit of the CALIPSO archive, so that full consistency within the archive is ensured. The new Version 2 of the IIR Level 1b products, which was released in July 2017, is available at the Atmospheric Science Data Center at NASA LaRC and at the AERIS/ICARE Data and Services Center in Lille (France). Version 2 IIR Level 1b products will be used to produce the future Version 4 of the IIR Level 2 products, which is being developed by the IIR science working group in France.
Version 4 IIR Level 2 products will also benefit from the improvements in the Version 4 CALIOP products presented in this special issue. The monitoring of the IIR calibrated radiances will continue using the new Version 2 and will be updated using MODIS Collection 6 data. In addition, the stand-alone approach will benefit from the most recent version of the 4A/OP model, a newly released version of the spectroscopic database (GEISA-2015), and will use a clear sky mask derived from Version 4 CALIOP and IIR products.
Acknowledgements The authors are grateful to  NASA LaRC  and to  SSAI (Science Systems and Applications, Inc. ) for their support. This work benefited from the support of the  Centre National de la Recherche Scientifique (CNRS)  and of  Institut National des Sciences de l'Univers (INSU). We thank  Brian Getzewich  and  Tim Murray  for the processing of the IIR Level 1b data at  NASA LaRC. We thank the  AERIS infrastructure  for providing access to the CALIPSO and REMAP products, and for data processing. We thank  Laurent Crépeau  ( Laboratoire de Météorologie Dynamique ) for generating IIR and MODIS 4A/OP simulations at the  AERIS/ESPRI (Ensemble de Services Pour la recherche à l'IPSL) Data and Computing Center of Institut Pierre Simon Laplace (IPSL).
Data availability CALIPSO IIR Level 1b data products are available at the Atmospheric Science Data Center at NASA LaRC (https://eosweb. larc. nasa. gov/project/calipso/calipso_table) and at the AERIS/ICARE Data and Services Center (http://www. icare. univ-lille1. fr). Collocated IIR and MODIS observations from the REMAP product are available at AERIS/ICARE. IIR internal calibration data and post-processed data are available upon request from the authors.
This accounts for losses in carbon from harvest to storage in geologic repositories. Unlike the wood products pools, the BECCS pool does not decay, hence captured carbon is kept from the atmosphere indefinitely. JULES simulations were performed on the N48 grid used in IMOGEN (2. 5°l atitude × 3. 75°longitude). This course resolution allows for many simulations to be run efficiently (each experiment was run with 34 separate climates for the 21st century, resulting in 170 century-long simulations). We regridded the IMAGE data using Patch recovery in the National Center for Atmospheric Research Command Language (NCL) Earth System Modelling Framework regridding toolkit  52. Landuse data from IMAGE was provided at a 0. 5°× 0. 5°resolution as fraction of crop, pasture, and bioenergy crop in each grid cell and regridded to N48 using NCL.
The regridding results in slightly more land area in the N48 resolution: 13345. 8 Mha compared to 12991. 1 Mha in the original resolution. The potential impacts on production simulated in JULES are small: land area for bioenergy crops in 2100 is 3% lower after regridding in IM1. 9 and 2% higher in IM2. 6  The ability of JULES to simulate natural vegetation and soil carbon dynamics has been previously validated  30, 53. We compare simulated and observed biomeaverage C veg and turnover time (defined as NPP/C soil ), following the method for calculating biome averages in Harper et al. 23  and after regridding from their native resolutions to the N48 resolution. The relevant observations are above/belowground biomass carbon  54  , NPP from MODIS  55, 56  , and a global dataset of soil carbon 57 (Table  2 ).
Acknowledgements The work was undertaken as part of the  UK Natural Environment Research Council's programme  "Understanding the Pathways to and Impacts of a 1. 5  °C Rise in Global Temperature " through grants  NE/P014941/1  (P. C. , A. H. , T. P. , J. H. , C. H. ,  S. Sitch , T. L. ),  NE/P015050/1  (E. C. -P. , G. H. , S. C. ), and  NE/P014909/1  (W. C. , C. W. , C. H. , P. C. ,  S. Sitch ). The authors also acknowledge support from:  EPSRC  Fellowship  EP/N030141/1  (A. H. );  NERC NE/P019951/1  (A. H. and T. L. ); the  Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ) (E. B. , A. W. , and E. R. );  CRESCENDO  ( EU  project  641816 ) (E. B. , A. W. , E. R. , and L. B. );  EU   FP7 LUC4C program  ( GA603542 ) (J. H. , A. K. ,  S. Sitch , and A. W);  CEH National Capability Fund  (C.
Code availability. JULES is an open-source model and the branch used in this work is available from the Met Office Science Repository using the following URL (registration required): https://code. metoffice. gov. uk/trac/jules/browser/main/ branches/dev/edwardcomynplatt/vn4. 8_1P5_DEGREES?rev=11764. Data Availability. The data that support the findings of this study are available from the corresponding author upon request. IMAGE scenario land-use data is also available from https://data. knmi. nl/datasets?q=PBL.
Results indicate that the IAGOS observations close to the surface do not appear to be strongly impacted by local emissions related to airport activities. In terms of mixing ratio distribution, seasonal variations and trends, the CO and O 3 mixing ratios measured by IAGOS in the first few hundred metres above the surface have similar characteristics to the mixing ratios measured at surrounding urban background stations. Higher in altitude, both the difference with data from the local AQN and the consistency with the GAW regional stations are higher, which indicates a larger representativeness of the IAGOS data. Despite few quantitative differences with Frankfurt, consistent results are obtained in the two other cities Vienna and Paris. Based on 11 years of data (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012), this study thus demonstrates that IAGOS observations in the lowest troposphere can be used as a complement to surface stations to study the air quality in/around the agglomeration, providing important information on the vertical distribution of pollution.