dataesr/scientific-paragraphs-categorization huggingface dataset:
However, to improve the relationship, the Rrs in the red band is multiplied by a ratio between the green and blue band Rrs signals. This relationship was tested on TUCPA field campaign dataset. It also varies linearly with SPM concentration but the linear regression shows a lower R 2 of 0. 56 compared to using the red band only. Moreover, a three bands relationship cannot be applied to SEVIRI data as only the red band is available, and would decrease the spatial resolution of MODIS to the 500 m resolution of its green and blue spectral bands. The three best relationships between in situ Rrs (sr ´1) and SPM concentration (g¨m ´3), obtained in the red spectral bands of the three satellite sensors OLI, MODIS and SEVIRI are presented on Figure  5  and reported hereafter: SPM " 1433 ˆRrs pOLI{655 nmq R 2 " 0.
g. ,  [8, 53] ) and (iii) measurement uncertainties. Nevertheless, these relationships are more robust than those proposed by  [54]  as they are based on a larger range of SPM concentrations (3-60 g¨m ´3 in this study compared to 1-10 g¨m ´3 in  [54] ) and show a slightly better determination coefficient (R 2 of 0. 61 for this study compared to 0. 56 in  [54] ). These relationships should be improved in the future with the acquisition of supplementary in situ data. First, the acquisition of new in situ data at low SPM concentration should increase the robustness of these relationships at low Rrs and SPM concentration signal. Secondly, based on the TUCPA dataset, these relationships seem linear up to 60 g¨m ´3, but more points at low and high SPM concentrations are needed to confirm this linearity.
1. 2). Some variations in the plume shape, orientation and extension are observed between the MODIS-T and MODIS-A contour lines. However, Figure  12  shows that the two images per day provided by MODIS are insufficient to describe the whole hour-to-hour dynamics of the plume highlighted by SEVIRI, mainly because of the close time between MODIS-A and MODIS-T overpasses (no MODIS image recorded after 13:00 UTC). Nevertheless, over low cloud cover periods, MODIS data provide a high spatial resolution monitoring of the daily variations of the river plume. Over partly cloudy periods, the higher temporal resolution of SEVIRI increases the number of available images and thus significantly improves the monitoring of the plume dynamics. Figure  12  illustrates the capabilities of SEVIRI and MODIS to capture the hourly and daily dynamics of SPM in the river plume during the study period.
These short time variations (few hours) of plume shape under changing wind directions support a dominant and fast effect of wind stress on the river plume. These high frequency dynamics were already predicted by previous studies mainly based on modeling and in situ observations, and few satellite images (e. g. ,  [8, 17, [22] [23] [24] 57] ). These dynamics were here observed and confirmed for the first time based on high temporal resolution satellite data such as those provided by SEVIRI.
3. Despite differences in data processing (mainly atmospheric corrections), waveband ranges and radiometric characteristics, the three sensors tested provide consistent Rrs values in the red spectral region in such moderately turbid waters (Rrs > 0. 01 sr ´1). A maximum difference of ~16% (median value) was observed between SEVIRI and MODIS-A, as already reported for the southern North Sea  [12]. This difference is lower between SEVIRI and MODIS-T (~6%). For the highest Rrs values (> 0. 023 sr ´1), rather high differences were also observed between OLI and MODIS-A (9%) and -T (16%). They are mainly attributed to an underestimation of Rrs in the most turbid waters due to the MUMM atmospheric correction. 4. The high spatial resolution of OLI provides impressive details in terms of small-scale turbidity features.
5. Despite the micro-tidal environment of the Rhône River plume, significant short-term (hourly) variations in its plume shape and SPM concentrations have been highlighted in this study. These high frequency dynamics were already predicted through modeling and in situ data, as well as using few satellite data, but was for the first time directly observed and confirmed through high temporal resolution satellite data such as those provided by SEVIRI. Indeed, SEVIRI allowed highlighting significant diurnal variations of the plume shape, mainly controlled by river discharge, wind stress and regional circulation, and despite its low spatial resolution provides a suitable hourly to daily monitoring of the river plume dynamics. The two MODIS images per day occurrence allow to highlight trends in these hourly dynamics but only over a short time window (only 2 to 3 h) and before 13:00 UTC, which is not sufficient to reproduce the Rhône River plume dynamics over a whole day.
Similarly, despite its high spatial resolution, the MODIS temporal resolution does not allow for the monitoring of high frequency (hourly) surface SPM concentration variations in the river mouth, highlighted by autonomous in situ probe data recorded at the Mesurho station. Nevertheless, MODIS temporal resolution is well adapted to monitor the daily dynamics of the plume shape and surface SPM concentrations. The low temporal resolution of OLI clearly prevents the monitoring of the dynamics of SPM in the Rhône River plume and the exploitation of its high spatial resolution for surface SPM fluxes estimation at the river mouth. These results clearly highlight the complementarily of these three sensors and emphasize the advantage of a combination of multi-sensors data for the monitoring of river plumes.
However, this monitoring is still limited by the low spatial resolution of SEVIRI and the low temporal resolution of MODIS and OLI data acquisitions. The next generation of polar-orbit satellite sensors, MSI onboard S-2 and OLCI onboard S-3, will provide data with a spatial resolution from 10 to 300 m and revisiting times from 5 to 1 day (respectively). In addition, the future Flexible Combined Imager (FCI) onboard the future geostationary satellite MTG-I will provide data with a spatial resolution of ~1 km and a higher revisiting time than SEVIRI (10 min). This new satellite sensors generation will allow developing new applications in coastal waters. These three sensors have red spectral bands suitable for SPM concentration retrieval, allowing their high frequency and high resolution monitoring in river plumes and coastal areas.
As already observed with OLI in the Rhône River plume, the high resolution of MSI associated to its higher temporal resolution should allow following daily to weekly variations of SPM concentrations in the downstream part of the river. This should largely improve our current understanding of SPM transport and estimation of SPM discharge from the river into coastal waters through the river mouth. The combination of MODIS (or its successor), MSI, OLCI and OLI should permit the observation of fine turbidity features and turbidity front contours with higher spatial and temporal resolutions, allowing us to better observe and understand sediment transport in coastal waters (e. g. , dilution of freshwater, settling of SPM, flocculation processes, etc. ). This will provide valuable data for the calibration and validation of sediment transport and biogeochemical models.
These relationships are based on an unprecedented dataset of in situ Rrs and SPM concentration measurements, as SPM concentrations as high as 60 g¨m ´3 were sampled for the first time, i. e. , not observed in previous campaigns  [8]. However, it still needs to be improved with new in situ measurements in order to better constrain the inversion of low Rrs values as well as to confirm the linearity of this relationship. Recently, the TriOS-RAMSES hyperspectral radiometers were mounted on the Mesurho station in order to record autonomous Rrs measurements. Preliminary results are promising (already tens of quality match-ups with MODIS satellite data) for the validation of atmospheric corrections of satellite data. A fully equipped autonomous station (with radiometers and backscattering sensors) should permit, in the near future, a better validation of both atmospheric corrections and retrieval of SPM concentrations.
Acknowledgments: This work was funded by the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement n  ˝606797  ( HIGHROC  project). USGS  and  NASA  are thanked for the Landsat-8 imagery. NASA is thanked for the  MODIS Aqua/Terra imagery  and the SeaDAS processing software. EUMETSAT and the  Royal Meteorological Institute of Belgium  are acknowledged for providing SEVIRI data. The field campaign was financially supported by the  TUCPA  project under the  EC2CO DRILL Programme  of the CNRS. We also thank the captain and crew of the  R/V Antedon II. We thank  Matthias Jacquet  and  David LeBerre  from  IFREMER , as well as  Sabine Marty  from  LOV  for their help during the cruise trip. The MesuRho station was supported by the  French INSU-EC2CO program TUCPA , the  MISTRALS MERMEX program  ( Marine Ecosystems Response in the Mediterranean EXperiment ), and the international LOICZ and PERSEUS ( EC grant  agreement  287600 ) projects.
Images were processed using AxioVision 4. 8. 2 software (Carl Zeiss AG, Oberkochen, Germany). Primary antibodies used were: rabbit anti-HCF-1 (1:1000, H12  (Wilson et al. , 1993) ), rat anti-Ki67 (1:60, eBioscience cat. # 41-5698), mouse anti-HNF4α (1:100, R&D Systems cat. # PP-H1415-00), rabbit anti-Histone H3 phospho Ser10 (1:100, Abcam cat. # ab5176), rat anti-BrdU (1:250, AbD Serotec cat. # OBT0030), and mouse β-catenin (1:50, BD Biosciences, cat. # 610153). Secondary antibodies used were: Goat anti-rabbit Alexa 488 (1:400, Molecular Probes cat. # A11034), goat anti-mouse Alexa 568 (1:500, Molecular Probes cat. # A11019), goat anti-rabbit Alexa 568 (1:1000, Molecular Probes cat. # A21069), goat anti-mouse Alexa 488 (1:400, Molecular Probes cat. # A11029), and donkey anti-mouse Alexa 594 (1:500, Molecular Probes cat.
Introduction. Large areas of land around the world have been impacted by former industrial and other anthropogenic activities. These include urban brownfield sites, former mining or resource extraction sites, and urban and rural areas affected by diffuse contamination. For example, there are estimated to be close to one million (or more) potential brownfield sites across the European Union  (Oliver et al. , 2005) , a considerable fraction of which may have real or perceived contamination problems  (Panagos et al. , 2013). The German register of contaminated sites lists about 300,000 potentially contaminated sites (UBA 2015), while the French Basol database references 6319 polluted sites requiring government action, mainly located in Rhone-Alpes (17%), Nord-Pas-de-Calais (10. 5%), Aquitaine (9%) and Ile de France (8.
The GREENLAND project -overcoming the impediments to practical application. The GREENLAND project has adopted a transparent and simple plan of action to address these impediments. It has co-ordinated data collection from a range of long-duration GRO pilot projects at contaminated sites across Europe, evaluated standard protocols and methods for site monitoring, and developed a set of specific design aids (for use when GRO appear to be a viable option) to promote the appropriate use of gentle remediation options and encourage participation of (and inform) stakeholders. The specific aims of the project were to: 1. Assess the efficiency of GRO via long-term (> 5 year duration) field trials; 2. Test the possibilities for biomass valorisation at trace element contaminated sites; 3. Evaluate a set of soil tests to assess GRO performance or "success"; 4.
GRO application may also require additional technical input from agronomists and plant specialists, institutional or planning controls to avoid shifts in land-use or land management, and a supportive local/regional regulatory framework (in cases where contaminants are stabilised in the ground, rather than removed (e. g. phytostabilization, in-situ immobilization/phytoexclusion) or where only the bioavailable contaminant fraction is removed (e. g. in phytoextraction)). The GREENLAND best practice guidance and DST summarise conceptual site models and supporting data for a series of longer-term trial sites drawn from the GREENLAND network, which have shown effective risk management for different metal(loid) contaminants under contrasting site conditions. Such pilot-scale applications of effective GRO strategies (i.
e. "success stories") are key in providing robust technical and practical data for GRO implementation and in engendering confidence in stakeholders, both in terms of illustrating the long-term risk management potential of GRO but also in showing how wider economic, environmental and societal benefits can be realised. Pilot sites can also be pivotal in education and training as demonstrator sites, both for specialists (e. g. regulators, contaminated land consultants) and non-specialists. For example, the BIOGECO phytoremediation platform is a large Cu-contaminated wood preservation site located in the Gironde, SW France (and part of the GREENLAND site network) which has been used as a pilot site since 2006 for phytostabilisation, aided phytostabilisation (combining in situ stabilisation and phytostabilisation), (aided) phytoextraction, and PAH phytodegradation.
Acknowledgements. Further details of the  GREENLAND  project, and downloadable versions of the GREENLAND decision support tool and GRO best practice guidance, can be found at http://www. greenland-project. eu. The authors are grateful for financial support from the  European Commission  under the  Seventh Framework Programme for Research  ( FP7-KBBE-266124 ), and thank the wider  GREENLAND consortium  and project advisory board for their support and input during the development of the DST and GRO best practice guidance.
Furthermore, we used size distribution data from a scanning mobility particle sizer (custom-built SMPS system, Lund University) collected at the ZF2 site during the wet season (Jan-Jun) in the years of 2008-2009, comprising measurements from 133 days  42  to investigate observed bursting events of nanoparticles in the Amazon basin. Particle size distribution measurements were performed 10 m above the canopy top. All measurements were performed under dry conditions (RH 30-40%), assured by an automatic diffusion dryer. 43 RESULTS AND DISCUSSION Size Distribution and Chemical Composition of Fungal Spores. Typically, fungal spores are a few micrometers in size, often spherical, rod-like or spheroidal (prolate) in shape. 17, 44, 45 Figure  1  shows representative morphologies of fungal spores found in the Amazonian samples.
The structural proteins are strongly bound in the wall due to covalent bonds, remaining attached even after salt extraction  [10]. Cell wall structural proteins are a small fraction of the total extracellular proteins so far detected. Indeed, of the 281 extracellular proteins present in databases in 2005, Jamet et al. [10]  referred that only 1. 5% of extracellular proteins were structural proteins. The crosslinking of the structural proteins to the wall polysaccharides is a dynamic process in the cell, developmentally controlled and influenced by the environment. Thus, its alteration affects the wall rigidity and resistance to degradation, with marked implications on the properties and behavior of the wall, extending from cell elongation to many developmental processes and abiotic and biotic plant responses  [6, 11].
A Proteomics Approach to the ECS The increasing knowledge on the ECS has reinforced the view of the fundamental role played by this compartment for cell function and plant metabolism. Information on the ECS proteins was initially based on classical biochemical techniques subsequently greatly improved through the use of proteomic methodologies. Two-dimensional gel electrophoresis (2-DE) is the most common technique used for protein separation and mass spectrometry (MS) for their identification. The 2-DE delivers a map of total protein fraction that reflects changes in protein expression level, isoforms, or post-translational modifications (PTM). The successful identification of the ECS proteins is dependent on extraction and separation, amenability to MS analysis, and protein annotation in the sequenced databases.
Bibliographic Search (Description of the Paper Dataset of the Review) A bibliographic search on the Web of Knowledge (http://apps. webofknowledge. com, 1 December 2015) was made using as search topic "plant apoplastic proteom*". This survey identified 83 papers that used proteomics methodologies for the study of ECS proteins of Angiospermae. When considering only peer-reviewed original research articles, this number was reduced to 52 papers (Supplementary Table  S1 ). Five out of the 52 papers integrated other -omic methodologies: transcriptomics (two papers;  [36, 37] ) and metabolomics (three papers;  [38] [39] [40] ). More than half of these papers are on leaf (60%), followed by stem (7%), seedlings (7%), and root (6%) (Figure  2A ). Although xylem is an apoplast component, only two papers, both on Brassica oleracea, specifically consider it  [38, 41].
The papers we analyzed date from 2003 and the most popular method for protein fractionation used was 2-DE (39 out of 52). Differential in-gel electrophoresis (DIGE) was used in six papers. Starting in 2009, some papers combine both gel-based (1D) and gel-free methodologies for plants with sequenced genomes. When different conditions of stress (abiotic and biotic) and non-challenged were considered, the lack of proteomic data on different organs led us to focus on leaves. The taxonomy of our dataset (leaf) shows that the commelinid monocot plants were represented by several genus of just the Poaceae family, while the dicotyledoneae (dicot) plants were represented by different genus and families: Brassicaceae, Fabaceae, Rubiaceae, Solanaceae, Actinidiaceae, Salicaceae, Amaranthaceae, Rosaceae, and Vitaceae.
These leaf papers can be divided by experimental conditions: nine papers on abiotic stress, 15 on biotic stress, and eight on non-challenged plants (Figure  2B ). To extract the same type of information on the proteins described in the papers we faced the difficulty of the insufficient descriptions of datasets and different terminologies used by the several authors, problems already reported by Jorrin-Novo  [42]. For this reason we were not able to perform a bioinformatic functional annotation of the proteins (e. g. , BLAST2GO) but, instead, we did a manual inspection of the data. Consequently, when no annotation was given by an author we ascribed a biological process to the protein on the basis of the information provided in the dataset (Table  S1 ). The major limitation of this approach was that each protein was only ascribed to one biological process, when, potentially, it could be involved in several processes.
Protein Metabolism In our review, of the proteins ascribed to protein metabolism the majority are proteases. They are mainly of the serine-, cysteine-, and aspartyl-type, but not threonine-or metallo-proteases. Data available at the MEROPS database  [80, 81]  (Supplementary Table  S2 ) show that, at the genome level, the serine-type proteases are typically the most abundant, followed by the aspartic-type and cysteine-type. We have also detected a higher amount of serine-type proteases (subtilisin and serine carboxy peptidases), followed by aspartic-type proteases and cysteine-type proteases. In the dicot plants, proteolysis seems to be more important for biotic stress and non-challenged conditions than for abiotic stress, for which serine-type proteases, particularly subtilisins, have a major contribution.
Redox It is known that the ECS has a significant contribution to the redox-signaling network in plants  [89]. By analyzing the paper dataset of this review, we found that about 8% of proteins are involved in the redox metabolism, independently of the experimental conditions (abiotic, biotic, or non-challenged, Figure  3 ), and are mostly peroxidases and SOD. Peroxidases are important oxidoreductase enzymes responsible for the stress-induced formation and degradation of ROS ( ‚ OH, HOO ‚ , H 2 O 2 ). They are of significant relevance in both development and response to the environment, being involved in lignification, suberization, auxin catabolism, wound/healing, and defense against pathogens  [90]. The major peroxidases present in plant ECS are of class III, for which more than 100 genes were found in plant genomes.
Several groups or families of kinases are present in plant genomes and are involved in a wide range of functions, from cell signaling to metabolism. Some of these kind of proteins found in our APF dataset are serine-threonine protein kinase, leucine-rich repeat receptor kinase (LRR-receptor kinase), CLAVATA1, DUF26, EP1/AED19, Ca/calmodulin-dependent protein kinase, and mitogen-activated protein kinase. Proteins with a LRR-domain, such as lectins-like proteins, were found in the APF of Arabidopsis and potato  [12, 36, 39, 57, 100]. The LRR domain (a sequence of 20-29 amino acid residues) is a versatile structure that favors the formation of protein-protein and/or -polysaccharide interactions. LRR proteins are numerous and include the, LRR-containing receptor-like kinases (LRR-RLKs), LRR-containing receptor-like proteins (LRR-RLPs) and polygalacturonase inhibitor proteins (PGIPs) and have diverse functions  [101]  with a prominent role in plant defense, among other biological functions.
It is known that upon perception of microbial signals, kinases, and phosphatases target specific proteins, often modifying complex signaling cascades that allow for rapid defense responses  [79]. The presence of phosphatases in the extracellular proteome of Arabidopsis infected with Pseudomonas syringae suggests that potential phosphorylation/dephosphorylation reversible regulation could occur in the apoplast  [113]. Aldose-epimerase proteins were categorized in minor carbohydrate metabolism, with a molecular function of carbohydrate-binding or isomerase activity. They can be involved in the formation of carbohydrate derivatives by the addition of a carbohydrate residue to another molecule. They interact selectively and non-covalently with any carbohydrate, which includes monosaccharides, oligosaccharides, and polysaccharides, as well as substances derived from monosaccharides by reduction of the carbonyl group (alditols), by oxidation of one or more hydroxy groups to afford the corresponding aldehydes, ketones, or carboxylic acids, or by replacement of one or more hydroxy group(s) by a hydrogen atom (UniProtKB database).
Supplementary Materials:The following are available online at www. mdpi. com/2227-7382/4/3/22/s1, TableS1: Original research articles in plant apoplastic proteins. The bibliographic search on the Web of Knowledge (http://apps. webofknowledge. com, 1 December 2015) was made using as search topic "plant apoplastic proteom*". TableS2: Known and putative proteases available at the Merops database (accessed on 12 March 2016).
Biomacromolecules Article stability heating controller, and a Specac electrical heating jacket. The sample was acclimatized at each temperature point for 10 min before measurements were taken. Spectra were scanned 128 times over the range of 900-4000 cm -1. X-ray Diffraction. X-ray diffraction was performed on a peptide stalk prepared by drawing a fiber of 1% conjugate solution between the ends of wax-coated capillaries, after separation and drying a stalk was left on the end of one capillary. For some samples, the capillary was mounted vertically onto the four axis goniometer of a RAXIS IV++ Xray diffractometer (Rigaku) equipped with a rotating anode generator (CuK α radiation, wavelength λ = 1. 54 Å). The XRD data was collected using a Saturn 992 CCD camera. The sample-to-detector distance was 50 mm.