dataesr/scientific-paragraphs-categorization huggingface dataset:
When the amplification of an ORF was unsuccessful the exons were amplified separately using genomic DNA as template. The exon fragments were then fused by PCR in order to reproduce the ORF sequence that is described at the TAIR database (http://www. arabidopsis. org; TAIR 9). Primers (from Eurofins MWG Operon) are listed S5 Table. pDONR207 constructs were sequenced to ensure ORFs integrity. From the pDONR207 vectors containing the ORFs an ordered library has been build. Examples of ORFs functional validation are given S6 Fig.
Precision, recall and false discovery rate calculations Precision (P, estimate the proportion of true positive), recall (R, estimate the true positive rate) and false discovery rate (FDR) calculations associated with our Y1H dataset were based on published positive and negative interactions (S1, S2 and S4 Tables). P = TP / (TP+FP), R = TP / (TP+FN), and FDR = FP / (TP+FP). TP: true positive (confirmation in our dataset of published data), FP: false positive (interaction identified in our dataset that was published as not occurring), FN: false negative (published interaction that was not confirmed in our dataset).
Bioinformatics Phylogenetic tree and binding matrix. R2R3-MYB protein sequences were aligned with clustal-omega  [47]  and only the block corresponding to R2R3 domains exactly as defined by  [3]  was conserved for further analysis. Then the LG substitution model with fixed gamma and invariant rates was used to build a maximum likelihood phylogeny with phylogeny. fr [75]. The tree was then edited and branches collapsed with FigTree (http://tree. bio. ed. ac. uk/software/ figtree). The resulting subgroups were named trying to maximize the overlap with previous MYB classifications. R library corrplot was used to plot a matrix of mean binding affinities inferred in the Y1H assays. Sequence logos. LOGOs of MYB recognition α-helices of protein members of a subgroup were generated with WebLogo  [76].
For the target cis-elements, those bound with affinity '++' by at least two thirds of a subgroup were used as input for WebLogo aligned as in Fig  1. Protein-DNA interfaces. The interfaces of Protein Data Bank entries 1MSE  [49]  and 2KDZ  [77] , as dissected by 3d-footprint  [78] , were aligned with tfcompare  [79]. Mutual information calculation. Columns in multiple alignments of recognition helices and also in multiply aligned cis-elements of clusters of subgroups in S5 Fig were used to calculate first the frequency of independent occurrence of individual amino acids and DNA bases and then their co-occurrence frequency. Mutual information was then calculated with the standard equation with ln as logarithmic function.
Acknowledgments Part of this work was supported by the  French National Research Agency  ( CERES , Grant  ANR-BLAN-1238 ) and the  PLANT-KBBE Initiative  ( STREG ,  ANR-08-KBBE-011-01 ). http:// www. agence-nationale-recherche. fr/. The work of Z. K. , F. S. and A. S. was supported by the  STREG  project. W. X. was supported by a fellowship from the  China Scholarship Council (CSC). tt2 myb5 double mutant seeds were kindly provided by  Dr Alan Lloyd.
Part of this work was supported by the  French National Research Agency  ( CERES , Grant  ANR-BLAN-1238 ) and the  PLANT-KBBE Initiative  ( STREG ,  ANR-08-KBBE-011-01 ). http://www. agencenationale-recherche. fr/. The work of ZK, FS and AS was supported by the  STREG  project. WX was supported by a fellowship from the  China Scholarship Council (CSC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Diseases caused by OXPHOS assembly defects After the discovery of the yeast ATP12 gene and its role in the assembly of the catalytic head of CV  (Ackerman and Tzagoloff, 1990) , sequence comparisons identified a similar gene in human cDNA databases (called ATPAF2) that proved to function like its yeast counterpart, as tested by heterologous complementation  (Wang et al. , 2001). A mutation in ATPAF2 was subsequently found as being responsible for the death of a 14-month-old girl who had severe neurological defects due to a low CV content  (De Meirleir et al. , 2004). Similarly, after the discovery of the yeast proteins Bcs1p [full name: ubiquinol-cytochrome c reductase (bc 1 ) synthesis]  (Cruciat et al. , 1999)  and Mzm1p (mitochondrial zinc maintenance)  (Cui et al. , 2012)  as being required for the maturation and/or insertion of the Rieske iron-sulfur protein (Rip1) into CIII, the homologous human genes, BCS1L and MZM1L, were considered as obvious candidates in individuals with nDNA-based CIII-assembly defects.
Databases used to compile this table: UniProt (http://www. uniprot. org/), CilDB (http://cildb. cgm. cnrs-gif. fr/), SGD (http://www. yeastgenome. org/) and ORFANET (http://www. orpha. net). All the listed nuclear genes encoded mitochondrial proteins except those marked by §, which are cytosolic proteins. The gene marked by * is absent in S. cerevisiae but present in Schizosaccharomyces pombe H. s. , Homo sapiens; S. c. , Saccharomyces cerevisiae ; % id, percentage identity.
Funding This work was funded by grants from  FRM (Fondation pour la Recherche Medicale)  to J. -P. d. R. , M. B. , V. P. , P. R. , A. R. and A. D. ;  AFM (Association Française contre les Myopathies ) to J. -P. d. R. , M. B. V. P. , P. R. , A. R, A. D. and G. D. ;  E-Rare  project  GENOMIT  to A. R. ;  National Science Center of Poland  ( UMO-2011/01/B/NZ1/ 03492 ) to R. K. ;  Kjer France ,  Ouvrir les Yeux ,  Retina France  and  Union Nationale des Aveugles et Deficients Visuels  to P. R. and V. P. ;  NIH (National Institutes of Health)  and  ERC (European Research Council ) to L. M. S. ;  Agence Nationale de la Recherche  ( ANR-12-BSV8-024 ) to A. D. and A. R. ; N. S. was supported by a doctoral fellowship from the  Ministere Français de l'Enseignement Supeŕieur et de la Recherche ; L. P.
A vast interest has created to unravel how NLRP3 becomes activated, since mechanistic insight is the prerequisite for a knowledge-based development of therapeutic intervention strategies that specifically target the NLRP3 triggered IL-1β production. In this review, we have updated knowledge on NLRP3 inflammasome assembly and activation and on the pyrin domain in NLRP3 that could represent a drug target to treat sterile inflammatory diseases. We have reported mutations in NLRP3 that were found to be associated with certain diseases. In addition, we have reviewed the functional link between NLRP3 inflammasome, the regulator of cellular redox status Trx/TXNIP complex, endoplasmic reticulum stress and the pathogenesis of diseases such as type 2 diabetes. Finally, we have provided data on NLRP3 inflammasome, as a critical regulator involved in the pathogenesis of obesity and cardiovascular diseases.
An aberrant activation of the NLRP3 inflammasome has also been observed in the case of other autoinflammatory diseases such as gout, pseudogout, silicosis, and asbestosis. In these cases, the aberrant activation is not caused by an inherited mutation but by chronic exposure to inflammasome activators such as MSU (causing gout), calcium pyrophosphate dihydrate (CPPD, responsible for pseudogout), or inflammation-inducing dust  [76]. The majority of the over 90 disease-associated mutations causing HPFs reside in the NBD domain of NLRP3, but not in residues predicted to directly interact with nucleotides. By using the structure of the N-terminal ATPase domain of Cdc6 (PDB code: 1FNN)  [77]  as template to model the NBD domain of NLRP3, Albrecht et al. showed that almost all of the disease-associated mutations comprised in the aligned sequence (V200-L371) cluster in the proximity of the nucleotide-binding region.
The role of ER stress in cardiovascular diseases has not been extensively studied. However, there is increasing evidence suggesting that ER stress impairs endothelial dysfunction through initiation of oxidative stress, inflammation and endothelium apoptosis. Therefore, ER stress appears to be important in the pathogenesis of atherosclerosis and myocardium ischemia-reperfusion  [166]. Recent data suggest that TXNIP is required for NLRP3 inflammasome activation and the release of IL-1β in endothelial cells in mice fed an HFD causing oxidative and inflammatory stress, obesity and retinal microvascular degeneration  [167]. Although TXNIP/NLRP3 inflammasome activation implicated in the endothelial dysfunction, is regulated by AMPK activity. This anti-inflammatory factor is highly implicated in the maintenance of endothelial homeostasis, the activation of endothelial nitric oxide synthase, the induction of endothelial NO production and the protection of endothelial function against oxidative stress  [168].
Accordingly, the continuous availability of Sentinel-2 (with Landsat 8 and Sentinel-1) time series will constitute a major advancement for large scale agriculture monitoring capabilities using satellite remote sensing. It will allow providing unprecedented estimates on crop area extent, crop type and state, which can serve as indicators for the agricultural productivity of the respective region. This paper is a first look at the Sentinel-2 for Agriculture (Sen2-Agri) project which was launched by the European Space Agency (ESA) in 2014 to anticipate this momentum and prepare the exploitation of Sentinel-2 data for local to global operational agriculture monitoring. The presentation begins with a project overview, objective, outputs, planning and approach. Then it focuses on the building of composite data sets consisting of satellite imagery and in situ measurements allowing development of agriculture monitoring methods for Sentinel-2.
Project Outputs The Sen2-Agri project delivers different products: (i) a core of processing strategies and (ii) an open source processing software developed from the OrfeoToolbox [19] to convert the Sentinel-2 Level 1c (L1c) data (i. e. , geo-coded top of atmosphere reflectance product associated with cloud and land/water masks  [15] ) into relevant EO products. These Sen2-Agri EO products consist of a suite of four validated Sentinel-2 based outputs (Figure  1 ):.
Selection Three sensors were considered to build the EO time series: SPOT 4, Landsat 8 and RapidEye. Characteristics of these three sensors are described in Table  4. SPOT 4 images were those acquired by the SPOT4 (Take 5) experiment which took place between February and June 2013  [18]. Priority was given to this SPOT4 (Take 5) data set, as it aimed to provide time series of optical images simulating the periodicity, the resolution and the large swath of Sentinel-2. Landsat 8 images (with less than 70% of cloud cover) were then considered as a complement to increase the temporal resolution of the SPOT4 (Take 5) time series and the time period covered by the time series when relevant. Finally, RapidEye data were selected only over specific sites during specific periods when there were no SPOT4 (Take 5) or Landsat 8 data.
Pre-Processing The SPOT4 (Take 5) data were made available as L2A products (see Table  4 ). The reader is referred to  [18]  for the details of the SPOT4 (Take 5) pre-processing methodology and validation. One particularity of the methodology comes from the Multisensor Atmospheric Correction and Cloud Screening (MACCS) processor, which uses multi-temporal criteria to build the various masks and to detect the aerosols before the atmospheric correction  [37] [38] [39]. Landsat 8 data were acquired at level L1T (see Table  4 ) in the UTM projection from the Earth Explorer website  [40]. They were pre-processed at their original spatial resolution to L2A using nearly the same method as the one developed for the SPOT4 (Take 5) images, thus also involving the MACCS processor  [41]. The processing was slightly enriched compared to the SPOT4 (Take 5) since additional bands were available: band 9 (cirrus band) enabled an enhanced detection of high and thin clouds and band 2 provided an additional criterion to detect aerosols.
L2A products were generated for the visible, near-infrared and shortwave infrared bands (bands 1 to 7). The thermal bands were not processed, as they are not available on Sentinel-2. RapidEye data were acquired as L3A products (see Table  4 )  [42]. After a resampling to the Sentinel-2 10 m resolution, they were processed to L3A BOA (see Table  4 ). In this case, the processing was slightly different since the multi-temporal method implemented in MACCS requires data acquired under constant viewing angles, which is not the case for the RapidEye sensor. The cloud detection was performed through a semi-automatic approach based on a Supervised Vector Machine active learning algorithm embedded into a QGIS plugin. The cloud shadows were then retrieved assuming a constant cloud altitude within the image and by searching the ground displacement for the best match with decreased reflectance values due to shadows.
When a significant difference between the expected reflectance value and the observed one was detected, the two consecutive images were visually analyzed. The analysis resulted in very good geometric accuracy for all data sets. When an anomaly was observed, it was mainly due to undetected clouds or shadows, but also to seasonality or changes in the brightness that were not related to the co-registration. Finally, the geometric consistency between images acquired by different sensors was analyzed. In general, the images of the different sensors were properly aligned. The geometric consistency between the SPOT4 (Take 5) and Landsat 8 images was naturally ensured since the SPOT4 (Take 5) ortho-rectification used the Landsat 8 images as reference for all sites. The only exception is the French site as already mentioned.
The consistency of the RapidEye data set with SPOT4 (Take 5) and Landsat 8 images was quantified (Table  5 ). The figures were obtained through a set of 90 points for the SPOT4 (Take 5) analysis and a set of 116 points for the Landsat 8 analysis. In both cases, the points were spread over all the RapidEye images (between 2 and 5 points by image). The results are below the SPOT4 (Take 5) and the Landsat 8 pixel resolution, which means that the three data sets could be used together.
‚ Cloud masks The quality of the SPOT4 (Take 5) cloud masks was assessed outside of this project. The protocol and the results are documented in  [18]. The evaluation of the clouds and cloud shadows masks relied on a visual verification of quick-looks. In spite of some imperfections, even very thin clouds and their shadows were usually detected. The quality of the cloud masks generated for the Landsat 8 and RapidEye data was assessed also by visual inspection. In general, for the two sensors, the performance of the cloud screening is good, even if some problems were noticed when dealing with small clouds. When working with Landsat 8 data, clouds identification is slightly improved when using the additional spectral information provided by the cirrus band 9. Figure  4a    This good performance of the cloud mask could be even better by further improving the mask expansion process.
Remote Sens. 2015, 7 18 the stable condition in the legend of Figure  5 , represented by blue circles. The highest disagreements were observed for the Tunisian site, which is not part of the project. The standard deviations for the Sen2-Agri sites were less than 0. 06. (a) (b) Figure  5. Validation of AOT estimates at 550 nm determined for (a) SPOT4 (Take 5) images (from  [18] ) and (b) Landsat 8 images using Aeronet in situ measurements. The same assessment was performed for the Landsat 8 data (Figure  5b ). The Aeronet sites that were used are: Seysses, Le Fauga and Avignon in France; Kyiv in Ukraine and Saada and Ouarzazate in Morocco. Here again, the accuracy of aerosol estimates was excellent. Despite the reduced overpass frequency offered by Landsat sensors (16 vs. 5 days), the same level of accuracy is obtained as for the SPOT4 (Take 5) data set.
• Temporal consistency of surface reflectance values The temporal consistency of the TDS was assessed by visually analyzing the temporal profiles of the individual reflectance bands and of the NDVI coming from different agricultural fields. When unexpected reflectance values were observed in the profiles, the images were visually checked. This analysis was done per site for each sensor, looking at the temporal profiles coming from the average of all pixels belonging to one field. In general, the detected outliers were explained by undetected clouds or shadows, the presence of aerosols, etc. Two issues were noticed in the SPOT4 (Take 5) data sets of Maricopa (United States) and San Antonio de Areco (Argentina), which could be explained by irrigation events (Figure  6 ). In the case of Maricopa, irrigation explained variations observed in the band 4 (shortwave infrared) for the dates of 15, 20 and 25 April.
The same occurred over the Argentinian image of May 29 when the The same assessment was performed for the Landsat 8 data (Figure  5b ). The Aeronet sites that were used are: Seysses, Le Fauga and Avignon in France; Kyiv in Ukraine; and Saada and Ouarzazate in Morocco. Here again, the accuracy of aerosol estimates was excellent. Despite the reduced overpass frequency offered by Landsat sensors (16 vs. 5 days), the same level of accuracy is obtained as for the SPOT4 (Take 5) data set. Unfortunately, no Aeronet sites were available near the Chinese and Pakistani sites where very large AOT were observed. For 2013, mean AOT values at 500 nm of 0. 719 and of 0. 706 were measured by the closest photometers of Lahore (Pakistan) and Beijing (China), respectively  [48, 49]. These figures must be taken cautiously as the photometers are at a significant distance from the agricultural sites and are located in cities.
‚ Temporal consistency of surface reflectance values The temporal consistency of the TDS was assessed by visually analyzing the temporal profiles of the individual reflectance bands and of the NDVI coming from different agricultural fields. When unexpected reflectance values were observed in the profiles, the images were visually checked. This analysis was done per site for each sensor, looking at the temporal profiles coming from the average of all pixels belonging to one field. In general, the detected outliers were explained by undetected clouds or shadows, the presence of aerosols, etc. Two issues were noticed in the SPOT4 (Take 5) data sets of Maricopa (United States) and San Antonio de Areco (Argentina), which could be explained by irrigation events (Figure  6 ). In the case of Maricopa, irrigation explained variations observed in the band 4 (shortwave infrared) for the dates of 15, 20 and 25 April.
The same occurred over the Argentinian image of May 29 when the reflectance values in the bands 1, 2 and 3 (green, red, near infrared) dropped in an unexpected way (up to 50% for the bands 1 and 2). Finally, the temporal consistency of the reflectance values between sensors (SPOT4 (Take 5) and Landsat 8) was evaluated, for the sites of France, Morocco and Belgium. Landsat 8 data were resampled to 20 meters and the same methodology as before was applied, thus analyzing temporal profiles made of two sensors. The conclusion was that the profiles coming from the two sensors were coherent and temporally consistent, as shown in Figure  7  for the Moroccan site.
In Situ Data Collection and Formatting In situ data for algorithm development and products validation were collected for the year 2013 by the team(s) responsible of each site. All site managers agreed to share their data with the project. Except for Belgium (where an official land cover database was already available for 2013) and United States (where annual cropland classifications exist as raster files), the cropland data (crop presence and crop type identification) were obtained by field observations during the main growing season of the year 2013. In order to have a complete data set allowing a proper validation of the cropland products, non-cropland classes were also requested and obtained either from field observations or from institutional data. Regarding the biophysical variables, only 4 sites had these kinds of data: Belgium, France, Morocco and Ukraine.
Over Belgium, France and Ukraine, LAI was obtained using the CAN-EYE imaging software  [50]  which extracts canopy structure characteristics from true-color images either acquired with a fish-eye or with a classic objective. Over Morocco, LAI was also estimated from hemispheric images using a MATLAB code which makes a similar work as CAN-EYE. Each data set received was checked, sometimes cleaned, and put in a standard format for the project.
Discussion and Future Activities The TDS was built using EO and in situ data acquired in 2013 over 12 globally distributed sites. The EO data set was made of the best possible images to simulate Sentinel-2 time series: the invaluable SPOT4 (Take 5) data collection, complemented by Landsat 8 and RapidEye data. Such combination of missions will be exactly the situation with Sentinel-2, especially during the first year when there will be a single operational satellite. The time series coming from the three sensors showed very good geometric performance. The specifications given in the literature  [18, 42, 44]  were largely met. Only the Landsat 8 data over the French site showed a lower geometric accuracy, with a 38-m bias. In addition, the multi-temporal registration was successfully assessed for each sensor and images acquired by different sensors proved to be properly aligned (Table  5 ).
The site selection and benchmarking approach adopted in this project has allowed algorithms and methods to be compared across sites  [23] [24] [25]. So far, very few examples of this kind of across-sites study exist. This can probably be explained by the logistics, coordination and costs needed to build the necessary data sets. Yet, these studies are highly important to improve our understanding of the current agriculture monitoring capacities. They are the prerequisite to bridge the gap between research and operational methods for agricultural remote sensing. With the past SPOT4 (Take 5) and the following SPOT5 (Take 5) experiments, the project provides a strong scientific contribution to the JECAM network and GEOGLAM initiatives: it allows completing one of the very first cross-cutting analyses for multiple, globally distributed sites.
Acknowledgments: This study has been conducted in the frame of the Sentinel-2 for  Agriculture  project funded by the  Data User Element of the European Space Agency. This work has been possible thanks to the  SPOT4 (Take 5 ) time series, which is the main source of the data set presented here. SPOT4 (Take5) data were provided by the  Centre National d'Etudes spatiales (CNES, France). Finally, the project acknowledges all site managers for sharing their data from the 2013 field campaign.
Assimilated observations The observations assimilated into each of the GOV forecasting systems in near-real time are detailed in Table  1. A variety of approaches is taken for use of sea surface temperature (SST) data with some systems directly assimilating swath data from the Group for High Resolution SST (GHRSST; Bluelink, GOFS and FOAM) and other systems assimilating or relaxing to objective analysis products from GHRSST. Most of the systems also assimilate in situ SST data available over the Global Telecommunications System (GTS) from ships, moored and drifting buoys. The GOFS system additionally assimilates Coastal-Marine Automated Network (C-MAN) in situ data available around the North American coast. Along-track altimeter sea level anomaly (SLA) data are obtained from Aviso/CLS by most systems (directly or through MyOcean), Bluelink obtain the data from the Radar Altimeter Database System (RADS) and GOFS use data processed by the US Naval Oceanographic Office (NAVOCAENO).