dataesr/scientific-paragraphs-categorization huggingface dataset:
Primer extension mapping of the psbB and psbH 5 0 UTR was performed with 10 mg of RNA, 32 P-labeled oligonucleotides and M-MLV reverse transcriptase (Promega) at 50 C essentially as described  (38). Chlamydomonas sRNA data were retrieved from Ibrahim et al. (39) , available from the National Center for Biotechnology Information as GEO Series GSE17815, including reads from four different libraries: two libraries had been prepared from a knockout line of the terminal nucleotidyltransferase and two libraries from the knockdown of a tryptophan synthase beta subunit. Three further libraries from the Comparative Sequencing of Plant Small RNAs project are available as GEO Series GSE32457. They had been prepared from a wild-type (WT) Chlamydomonas strain grown under normal conditions, under phosphate starvation or under sulfate starvation.
A processing of remotely-sensed Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series at 1-km spatial resolution is established to estimate sugarcane yield over the state of Sã o Paulo, Brazil. It includes selecting adequate time series according to the signal spatial purity, using thermal time instead of calendar time and smoothing temporally the irregularly sampled observations. A systematic construction of various metrics and their capacity to predict yield is explored to identify the best performance, and see how timely the yield forecast can be made. The resulting dataset not only reveals a strong spatio-temporal structure, but is also capable of detecting both absolute changes in biomass accumulation and changes in its inter-annual variability. Sugarcane yield can thus be estimated with a RMSE of 1.
Goal and Objectives The present work aims to further contribute to the understanding of how remote sensing indicators can predict within-season sugarcane yields at the state level in Sã o Paulo. The relationship between low resolution SPOT-VEGETATION biophysical products, weather data and official state-level yield statistics for Sã o Paulo are studied through different metrics extracted from seasonal time series of fAPAR (fraction of Absorbed Photosynthetically Active Radiation) and weather data over the period 1999-2010. Special attention is paid to the spatial purity of the time series and its influence in the reliability of yield estimations. The possibility of taking into account seasonal changes in phenology associated with differences in the temperature regime is also explored by using thermal time instead of calendar time when computing the fAPAR metrics.
Remote Sensing Data Processing The remote sensing data consists of fAPAR time series derived from SPOT-VEGETATION optical remote sensing data at 1 km spatial resolution. This biophysical variable has been calculated following the CYCLight processing chain  [18]  based on the CYCLOPES algorithm  [19] , which uses radiative transfer modelling to relate vegetation spectral signature and biophysical parameters. The product is operationally available in the European Commission Joint Research Centre MARS remote sensing database  [20]  as 10-day composites (or dekads) with an archive starting October 1998. The composites are generated from daily fAPAR observations using the maximum value compositing (MVC) approach  [21]  but the day of observation is retained as a separate layer. In this study, these remote sensing time series are subject to particular processing steps, which are detailed hereafter, in order to increase quality and to ultimately retrieve yearly metrics for which the relationship with sugarcane yields will be explored.
Therefore, measuring time with thermal units, such as cumulated daily temperatures above a threshold (in growing degree-days), can have a considerable advantage over using normal calendar time, especially when analyzing data where the temperature varies from season to season or from one place to another  [30]. By plotting fAPAR profiles in thermal time it becomes possible to compare fAPAR attained at a given phenological state, thus limiting the effects of eventual shifts in crop development along the season associated to differences in annual temperature regimes. In the present work, thermal time is calculated from daily average temperature data in the ERA Interim global database produced by the European Centre for Medium-range Weather Forecasts (ECMWF). Since temperature data is provided at a daily time step, it is necessary to resample the fAPAR time series according to their actual date of observation instead of using the original 10-day time step.
Thermal time (tt) is calculated for every fAPAR time series based on daily temperature data from the corresponding 0. 5° by 0. 5° ECMWF grid cell using the following formula: (1) where T min,i and T max,i represent minimum and maximum daily temperatures for a given day i while T b stands for base temperature of sugarcane, here considered to be 9 °C. Thermal time is calculated continuously for every day during the entire study period running from the starting time (t 1 ) of 1 October 1998 up to the finishing time (t 2 ) set to 7 December 2010.
Temporal Smoothing The fAPAR time series still contain noise that can be caused by uncertainties in some of the processing steps such as cloud filtering, atmospheric correction or biophysical variable retrieval. A first filtering step is applied to remove extremely low fAPAR values, which typically result from undetected clouds or atmospheric noise, using a simple decision rule consisting in calculating the change in fAPAR for every successive observation and remove those where there is a drop of more than 0. 1, followed by a rise greater than 0. 02. This process is based on the assumption that, in normal conditions, the sugarcane biomass will not drop and rise dramatically in the brief time period between 2 dekads. The values of 0. 1 and 0. 02 have been chosen as an empirical rule of thumb that was found to be adequate after analyzing a random subset of time series in the dataset.
Secondly, a temporal smoothing is done with the Whittaker smoother  [31] [32] [33]. Although not well-known in the remote sensing literature, according to  [32]  this filter has a series of advantages over the more popular and well-known Savitzky-Golay filter. Perhaps the single most important advantage is that of balancing two conflicting goals, namely preserving data fidelity and minimizing the roughness of the smoothed time series, by adjusting a single parameter λ, whose value can be set automatically using cross-validation  [32]. It has been shown to perform well in remote sensing studies at sub-continental scale, notably in South America  [34]  and India  [35]. The Whittaker smoother is here applied twice on each fAPAR time series: once with the individual fAPAR values plotted in thermal time and once using normal calendar time (using the actual dates of the fAPAR observations and not the 10-day composite dates).
Exploratory Analysis of the Remote Sensing Dataset Prior to analyze the predictive capacity of the remote sensing dataset to estimate yield, an overview of the spatio-temporal information contained in it is illustrated in this section. Figure  7  provides the 5th, 25th, 50th, 75th and 95th percentiles of the ensemble of pixels with a crop specific spatial purity above or equal to 75%. These represent the overall expected behavior of sugarcane. Interestingly, during the austral winter between the last two seasons (in 2009), the biomass, as detected by the remote sensing fAPAR, did not fell as low as during the previous years, explaining why less metrics are available for the last two seasons (as seen in Figure  5 ). These unusually high fAPAR values observed at the end of the 2009 season are a consequence of the intense precipitation cumulated during the period between August and October 2009 in the northern half of the region, where most of the sugarcane is cultivated.
According to ECMWF data it rained 270 mm for that period, which is more than twice the seasonal values (105 mm). To further illustrate the spatio-temporal information contained in the dataset, the result of a k-means clustering algorithm applied to all (thermal) time series with π ≥ 75% is illustrated in Figure  8. A strong spatial structure is apparent. Clusters 3, 8 and 9 are mostly located in the North-Eastern part of the state, where the landscape is dominated by sugarcane and the climate is tropical savannah (Af), whereas clusters 5 and 7 are mostly located in the Southern part characterized by the humid subtropical climate (Cfa). Clusters 4, 6 and 10 generally seem to attain lower fAPAR values than the rest (as shown on the profiles in Figure  9 ) and seem to be in distinctively different geographic regions: Cluster 4 in the center North while the other two in the scattered points in the Western part of the state.
Discussion The results demonstrate that low resolution remote sensing time series reflect valuable information on the dynamics of sugarcane in the state of Sã o Paulo. SPOT-VEGETATION 1 km fAPAR data not only reveals a strong spatio-temporal structure, as shown by the clustering exercise, but it is also sensitive enough to detect both absolute changes in green biomass accumulation and changes in its inter-annual variability. This is shown respectively by the yield estimation accuracy of around 1. 5 t/ha (or 2%) without considering the trend, and about 0. 6 t/ha when the trend is taken into account. Despite the fact that the trend captures already most of yield variability, and using it for forecasting provides better results, being able to make accurate predictions based on the signal without requiring modeling the trend is encouraging.
Given the high quality of the Canasat data, a refinement of the approach should be pursued to provide a finer yield forecast by cleaning the multiannual fAPAR metrics dataset already constructed. The various Canasat maps should be used to remove all metrics related to non-sugarcane, namely those corresponding to places classified as under renovation during a given year and all metrics corresponding to a time and place where the class expansion is present in a subsequent year. Then, the remaining metrics could be analyzed based on the three categories of sugarcane, potentially showing a sensitivity of the remote sensing signal to the decrease in yield expected respectively in the categories expansion, renovated and ratoon. The characterization of the spatial response function of the remote sensing instrument could also be improved, but it is probably sufficient as it is for SPOT-VEGETATION, which is a push-broom sensor.
Applying the methodology presented here on MODIS data could be promising; given it has a spatial resolution which is four times finer, and already has a data archive which is long enough (since 2000). Some recent work is already exploring a similar approach on sugarcane by integrating time on MODIS NDVI data  [39]. However, MODIS is a whisk-broom sensor for which the observation footprint varies strongly according to the scan angle  [40] [41] [42] , requiring a more elaborate model of the spatial response in order to use the daily data as it is required in this paper. Another discussion point is that the accuracy of the methods presented has a general dependence on the reliability of official statistics which fAPAR metrics are related to. In Brazil, the private sector also reports regional yield estimations under the umbrella of the União da Indústria de Cana-de-Aç úcar (UNICA).
This may be an alternative and independent source of information which could be used to further validate and consolidate the present results. Based on this work, various paths for further improvements and research can be followed. A logical continuation would be to explore the possibility of near-real time forecasting at the regional level with the datasets used in this study. This could also be done based on the biophysical products that will be available from the future Copernicus services, the European Earth observation program. These will composed of the BioPar pre-operational chains designed under the Geoland2 project, and for which the version 2 is designed to work in near-real time conditions  [43]. Another potential perspective is to downscale the approach to see the predictive capacity of remote sensing information at the municipality level, for which official yield statistics are also available.
At that scale, the statistical relationship between fAPAR metrics and yield could be reasoned differently to limit over-parameterisation problem, by either fixing the slope or intercept regression coefficients per municipality, as investigated in  [44]. The clustering exercise done in this study also remained underexploited. The relation between the average metrics per cluster with state yield could be explored, as perhaps some cluster covering the main production zones have a more stable relationship with state-level yield. To avoid the dependency of the method to a pre-established sugarcane map, a classification exercise should be envisaged based on the Canasat maps, in order to see how the fAPAR dataset can accurately identify sugarcane from other land covers, and perhaps even identify the type of sugarcane it is, and thus perhaps detect yield differences between sugarcane types.
Conclusions The proposed post-processing of the Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series enables to provide metrics that can be used to accurately estimate sugarcane yield over the state of Sã o Paulo. The dataset has a strong spatio-temporal structure and is sensitive both to absolute changes in biomass accumulation and changes in its inter-annual variability. The capacity of estimating yield based on this dataset was shown to be influenced by various aspects, namely: (1) the way time is regarded (thermal or calendar); (2) the purity of the signal; (3) how the information is extracted from the time series (i. e. , the type of metrics); and (4) the timing of when the information is available. Concerning what type of metrics to use in the case of Sã o Paulo, the recommendations that emerge from this study are that TypeIII metrics (representing the observed length of the growing season) are to be preferred for thermal time, but they should be combined with another metric providing complementary information on the position in time of the curve, such as with metric TypeIt5 or TypeII55a.
15 mM of each primer, and 0. 5 units of Taq DNA polymerase (Qiagen). Thermocycles consisted in an initial denaturation step at 94uC for 49, followed by 38 cycles of denaturation at 94uC for 300, annealing at 60uC, for 300, and extension at 72uC for 19. The final extension was conducted at 72uC for 79. PCR products were visualized on a 1. 5% agarose gel, purified and directly sequenced in both directions on 3730xl DNA Analyzer 96-capillary sequencers (Applied Biosystems) at Genoscope (E ´vry, France). Sequences were edited and assembled using CodonCode Aligner software (CodonCode Corporation, Dedham, MA, USA). Sequences were deposited in GenBank under the accession numbers KF378583-KF378585 (fish) and KF378588-KF378589 (monogeneans).
Phylogenetic analysis of polyopisthocotylean monogeneans The data matrix was built from the published alignment of Olson & Littlewood  [30]  (available from http://ebi. edu. au/ftp/ databases/embl/align/ALIGN_000150. dat), restricted to the Polyopisthocotylea excluding Polystomatidae and Sphyranuridae (this corresponds to the group designated as Oligonchoinea in  [30] ; for equivalences of monogenean terminology, see Table  1  in  [4] ) to which were added two newly obtained sequences of 28S: one from Lethacotyle vera n. sp. (KF378588), and one from an unidentified species of Neomicrocotyle Ramalingam, 1960 (KF378589) from Caranx sexfasciatus. There is general agreement that the Oligonchoinea are monophyletic and that the Hexabothriidae are basal among them  [10, [30] [31] [32] , so the two hexabothriids of the original alignment were chosen as an outgroup.
We checked against WoRMS  [42]  (date: 14 May 2013), the list of species of Gastrocotylinea for which we could obtain illustrations of sufficient quality. Our database includes 120 species; 9 of these species were not in WoRMS; the remaining 111 species represented 78% of the 142 species included in WoRMS. The 120 figures are available in a Supplementary File. The statistical significance of differences between families was tested with Mann & Whitney U test.
Phylogenetic position of the new species A phylogenetic analysis of 28S sequences (Figure  9 ) show that the new species forms a clade (PP = 0. 99, BP ML = 95) with Neomicrocotyle pacifica (from Caranx hippos (Linnaeus, 1766) off Mexico  [30] ) and Neomicrocotyle sp. (our specimens from Caranx sexfasciatus off New Caledonia), the two other protomicrocotylids of the dataset.
Acknowledgments Various fish specimens were identified (from photographs) by  Ronald Fricke ,  Jack Randall ,  Michel Kulbicki ,  Samuel Igle  ´sias and  Bernard Seret. Eric Bureau , student in training, collected some specimens. David Gibson  ( BMNH ) helped with linguistic discussions, and in the acquisition of rare literature and comments about species in WoRMS. Patricia Pilitt  (USNPC) provided photomicrographs of the holotype of L. fijiensis, and  Takashi Iwaki  (MPM) kindly took photographs of additional museum specimens. Patricia Pilitt  (USNPC) and  Eileen Harris  ( BMNH ) are thanked for arranging specimen loans.
This project was supported by the '' Service de Systematique Moleculaire '' of  MNHN  ( CNRS UMS 2700 ),  the network ''Bibliotheque du Vivant '' funded by the  CNRS , the  MNHN , the  INRA  and the  CEA (Genoscope) , and a grant ( Action Thematique du Museum  (www. mnhn. fr)''Formes possibles, Formes realisees'') awarded by  MNHN  to JLJ and CR. (These grants have no numbers). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Soluble apoplastic protein patterns were then compared to whole leaf soluble protein profiles by 2D-PAGE analyses. Subsequent MALDI-TOF/TOF mass spectrometry of tryptically digested protein spots was used to identify proteins. This large-scale proteomic analysis established a well-defined proteomic map of whole leaf and leaf apoplastic soluble proteins, with 223 and 177 analyzed spots, respectively. All data arising from proteomic, MS and MS/MS analyses were deposited in the public database world-2DPAGE. Prediction tools revealed a high proportion of (i) classical secreted proteins but also of non-classical secreted proteins namely Leaderless Secreted Proteins (LSPs) in the apoplastic protein content and (ii) proteins potentially involved in stress reactions and/or in cell wall metabolism.
The VIC method has been used for AF protein recovery and shown to be suitable for proteomic analyses. 2D-PAGE is known to be a robust global approach to obtain a snapshot of the secretome  [3, 8]. Since the accomplishment of the V. vinifera genome sequencing, genome scanning using dedicated software allowed numerous protein predictions  [1]. However, their presence has still to be confirmed and the use of the public databases improved considerably the potential of grapevine proteome analysis  [9]. The present work aimed at evaluating the potential of subcellular proteomics for identifying proteins in the apoplastic compartment of grapevine leaf. Subcellular proteomics has the advantage not only of relating proteins to a functional compartment of eukaryotic cells, but also of reducing the complexity of the tissue protein extracts, which often prevents a satisfactory proteomic analysis.
Protein identifications Protein separation by 2D-PAGE and MS identification were performed in order to compare the protein profiles obtained from whole leaf (Figure  1A ) and AF (Figure  1B ), as reported by previous work  [8, 20]. Two clearly different protein patterns were obtained by 2D-PAGE, demonstrating an apparent subfractionation of the AF. Comparative analysis of the acquired gel images from 4 biological replicates was carried out using the Delta2D software. The analyses revealed a total of 448 spots in the whole leaf extract and 306 spots in the AF extract over a non-linear pH range of 3-10 and a size range of 15-120 kDa. Respectively 223 and 177 spots were analyzed by MS and MS/MS in order to construct whole leaf and apoplast 2D maps of V. vinifera. MS analyses yielded respectively 227 and 89 different positive identifications of proteins and their respective isoforms.
Data arising from gel analyses are deposited in the public database World-2DPAGE (accession number 0037-Mapping the proteome and secretome of Vitis vinifera leaves)  [21]. The corresponding proteins are also listed in the Additional file 1 for whole leaf proteins and in Additional file 2 for apoplastic proteins. Although the V. vinifera genome was recently sequenced  [1] , little is known about the protein profiles of grapevine. Numerous identifications were based on homology to proteins characterized in other species by Blast-p search of the NCBI nonredundant database  [22]. These identifications lead to the in planta detection of 66 V. vinifera predicted proteins, confirming their presence in grapevine leaf AF (Table  1 ). Protein pI and M r values were experimentally determined and compared to their theoretical values obtained from primary structure analysis (Additional file 1 and 2).
Global analysis of whole leaf and apoplast proteomes Identified proteins in both apoplastic and whole leaf protein extracts were classified according to their molecular function (Figure  3 ). The GO database (www. geneontology. org) provides a useful tool to annotate and analyze the functions of a large number of proteins. Categories were based on GO classification using AgBase, a unified resource for functional analysis in agriculture  [27]. Proteins were grouped according to plant GO-slim categories obtained   for molecular functions. Some of the GO classes were merged in order to simplify the classification. The molecular function classification covered a higher proportion of peptides with hydrolase activity (36. 8%) and carbohydrate binding pattern (4. 4%) in the AF extract compared to the whole leaf extract (11.
Protein secretion From the AF proteins, 66% were predicted with a signal peptide sequence using the TargetP software  [28] , compared to 5% for the whole leaf proteins (Figure  4 , Additional files 1 and 2). A similar percentage of proteins with a predicted signal peptide sequence was found in soybean (65%), but lower amounts were found in Arabidopsis (47%) and rice (37%)  [19, 29, 30]. Predicted cleavage sites within the N-terminal region of most proteins identified in the AF are additional support for the quality of the AF extraction. In the classical secretory pathway, proteins are synthesized in the endoplasmic reticulum before passing through the dictyosomes to be inserted by vesicles in the plasma membrane or secreted into the extracellular space. Among the secreted proteins, some can be linked to the cell membrane.
Conclusions The VIC method has been optimized to allow protein recovery from grapevine AF suitable for 2D-PAGE analyses. The large-scale proteomic analysis presented in this study established a well-defined proteomic map of whole leaf and leaf apoplastic soluble proteins. These two proteomic maps have been released in the public World-2DPAGE database to be used as interactive reference maps. To our knowledge, this is the first detailed proteome study of the grapevine apoplastic fluid providing a comprehensive overview of the most abundant proteins present in the grapevine apoplast. This proteome map of the leaf AF of Vitis vinifera should represent an essential complement to available genomic tools and should prove useful for systemic high-throughput studies on molecular processes in the apoplast such as molecular interaction between grapevine and its natural pathogens.