dataesr/scientific-paragraphs-categorization huggingface dataset:
Metabolite analysis and data processing Metabolite profiles were obtained using six independent biological replicates per treatment. Metabolite extraction was carried out as previously described for leaf extracts  (Fiehn and Weckwerth, 2003; Fiehn et al. , 2008; Rellán-Álvarez et al. , 2011). Dried extracts were derivatized as described elsewhere  (Fiehn et al. , 2008). Derivatized samples (1 µl) were injected randomly in split-less mode with a cold injection system (Gerstel, Mülheim an der Ruhr, Germany) and separated in a GC device (Agilent 6890, San Jose, CA, USA) using an integrated guard column (Restek, Bellefonte, PA, USA) and an Rtx 5Sil MS column (30 m×0. 25 mm, 0. 25 µm film thickness). The GC device was connected to a Leco Pegasus IV time-of-flight mass spectrometer (TOFMS) controlled with Leco ChromaTOF software v.
2. 32 (Leco, St. Joseph, MI, USA). Peak detection and mass spectra deconvolution were performed with Leco Chroma-TOF software v. 2. 25, and GC-MS chromatograms were processed as described previously  (Fiehn et al. , 2008). Metabolite data were normalized using the sample fresh weight and the sum of all metabolite peak heights in a single run, to account for small GC injection variations. The resulting data were multiplied by a constant factor in order to obtain values without decimal figures. Peaks were identified using the Fiehn Lib database (http://fiehnlab. ucdavis. edu/projects/FiehnLib/index_html). Metabolite spectra, including those of identified metabolites and unknowns, can be queried and downloaded at the Fe Chlorosis Database (fechlodb. fiehnlab. ucdavis. edu). Data were analysed to check for possible correlations between peak height values and peak variance, and since a positive correlation was found a log 10 transformation of the data was carried out to avoid variance-mean dependence  (Chich et al.
, 2007). Statistical analysis of the normalized data was carried out with Statistica software (v. 9. 0. StatSoft, Inc. , Tulsa, OK, USA). Only those metabolites present in at least 80% of the samples from at least one class were considered. Significant changes in metabolite levels were detected for each class using one-factor analysis of variance (ANOVA; P ≤ 0. 05). Metabolite response ratios were defined as the level in the class divided by the level in the control class; when ratios were <1, the inverse was taken and the sign changed. Only metabolites with mean response ratios (using the non-log 10 transformed data) >1. 5 or below -1. 5 were considered relevant and are discussed in this study. Multivariate analysis (supervised PLS) was used to study the clustering of the samples, as well as to find out which metabolites were responsible for the separation between classes.
For all putative functional annotations cited in the following sections: (i) the MAPMAN Arabidopsis pathway analysis program  (Thimm et al. , 2004) ; (ii) TAIR (The Arabidopsis Information Resource, TAIR Gene Model on www. arabidopsis. org, 16 June 2012); and (iii) the ARAMEMNON database (aramemnon. botanik. uni-koeln. de; Schwacke et al. , 2003)  were referred to. The focus was first on overlaps in mutant versus WT samples in all three comparisons (atfer1-3-4 versus WT, atfer1-3-4++ versus WT++, and atfer1-3-4++ versus WT). This overlap contained 54 differentially expressed genes (44 with P-value <0. 05), which reflects the influence of the ferritin null mutant background regardless of the Fe status of the plants (Fig. 1A ). Disposition of these genes into functional categories (Table  1 ) revealed that the biggest focus of transcript regulation was on stress-and redox-related functions, accounting for 28% of the total changes.
Comparison of the flower metabolomes of Col-0 and atfer1-3-4 Arabidopsis plants grown under ironsufficient or excess conditions The metabolite profile of whole flower extracts from the A. thaliana triple ferritin mutant atfer1-3-4 and the Col-0 WT, grown in control conditions and under excess Fe (atfer1-3-4++ and WT++, respectively), were studied using GC-MS. Only metabolites present in at least 80% of any sample class (in five out of six replicates) were taken into consideration. A total of 251 of such consistently present metabolites were detected, and 103 of them were identified using the Fiehn Lib database, whereas 148 were unknowns (a complete metabolite list is shown in Supplementary Table  S7  at JXB online). When comparing the metabolite profiles of the different sample classes with those of the WT, 10 identified metabolites showed significant changes (at P ≤ 0.
Of the 63 protein spots identified by two-dimensional IEF in  for atfer1-3-4++ versus WT++. To verify transcriptomic data, mRNA contents of CysP and XTH3 were additionally analysed by qRT-PCR as described in Supplementary Table  S1. Significance analysis by a Student's t-test revealed the same regulation as for DNA microarrays (data not shown). Overall, the multivariate statistical analysis (Supplementary Fig. S2 ) from proteomic and metabolomic data suggests that the mutant in control conditions clusters closely to WT plants grown in excess Fe, in agreement with the lower ferritin and higher Fe concentrations in flowers reported for this mutant  (Ravet et al. , 2009a).
Acknowledgements We thank  Professor Michael Grusak  ( BCM, Houston, TX ) for his advice and careful reading and editing of the manuscript, and  Karl Mayer  ( LMU München ) for excellent technical assistance with microarray hybridization. This work was supported in the framework of the  European Transnational Cooperation (Germany, France, Spain)  within the  PLANT-KBBE Initiative  funded by (i) the  Bundesministerium für Bildung und Forschung (BMBF , framework of the GABI initiative, FKZ: 0315458A  to KP), (ii) the  Spanish Ministry of Economy and Competitivity  ( MINECO EUI2008-03618  to JA. ) and (iii) the  Agence Nationale de la Recherche  ( ANR-08-KBBE-009-01  to J-FB). The study was also supported by the  MINECO  grant  AGL2010-16515  to JA.
This approach resulted in a strategy that is based on the parallel use of a series of control genes and calculation of normalization factors using statistical algorithms  [8, 11, 41]. It is necessary to validate the expression stability of a candidate control gene in each experimental system prior to its use for normalization. In this regard, several free software applications such as geNorm  [8] , NormFinder  [42]  or qBase  [43]  are used in order to identify the best internal controls from a group of candidate normalization genes in a given set of biological samples. To our knowledge, no investigations have been yet carried out for the identification of reference genes in table grape, one of the most important template fruit crops. In this work we used a data set obtained from a large RNA-Seq experiment of table grape segregants phenotypically and genetically diverse, belonging to a 'Ruby Seedless' x 'Sultanina' crossing, sampled at three phenotypic stages, anthesis, fruit-setting and berries of 6-8 mm diameter (the last one from plants treated or not with gibberellic acid).
Identification of putative reference genes Usually the search for reference genes in any plant species is based on the identification of orthologs of genes stably expressed in model plants, mainly from Arabidopsis thaliana  [44, 45]. In this case, we used our own information obtained from a massive sequencing assay done with 47 samples of the same species of interest, i. e. , table grape (Vitis vinifera L. ). This set of samples corresponds to 14 genotypes from which RNA was collected, combining different flower and berry developmental stages and treatments (see Methods section). Even when the main outcome of an RNA-Seq exercise is the identification of differentially expressed genes, in this case the same data set was used to search for putative reference genes, considered as that any gene that has a minimal expression level variation in every sample analyzed.
The data (Table  1 ) showed a high estimated coefficient of variation (CV > 40%, with a range of ~25%). This variation probably could be given by an intrinsic variation within the biological sample (phenotype, phenological stages and gibberellic acid treatment) or by sampling error, because the sources of variation are considered during the selection of genes as differentially expressed by edgeR package  [46]. Only a few genes (~8% of total NDE genes) had mean and CV values large enough to rule out sampling errors. This among-samples variation could be explained because the genotypic effect was not taken into account for the selection of NDE genes. In this study, each one of the 14 genotypes used could be differentially interacting with the other factors or conditions (phenotype, phenological stages and gibberellic acid treatment).
Primers design and analysis of the variability from threshold cycles value Primer pairs for qPCR were designed and subsequently evaluated on table grape cDNA. For 17 out of the 19 primer pairs designed, a single amplicon was observed by electrophoretic separation; each amplicon was sequenced to confirm the primer specificity. The primers for VvADH7 and VvSLP had to be excluded from the study as they produced two amplicons under the tested PCR conditions. All primers were designed with the following criteria: 20-24 bp length, GC content between 50% and 65%, product size in the range of 91-268 bp and melting temperature between 60-64°C (Table  5 ). Melting curve analyses of the 17 genes showed a single peak in each case, confirming that the primers amplified a single product (data not shown).
Similar results for these two genes were observed in cvs. Red Globe, Crimson seedless and Muscat of Alexandria, a set of genotypes representing at some extent the genetic diversity of table grapes  [51]. In addition, we evaluated the performance of these two reference genes in leaves with similar results as berries (data not shown). To complement this, we used geNorm algorithm to determine the most stable reference genes assuming that two ideal reference genes should not vary in comparison with each other in the different tested conditions. This algorithm calculates the average pair wise variation of a given candidate reference genes set with all other genes under evaluation and assigns a measure of its expression stability (M), based on which a ranking of candidate reference genes is produced  [8].
The geNorm software has been cited for many authors in relation to the identification or behavior of reference genes; this is because of its easiness, robustness, reliability and convenience of use, and so it is currently included in qRT-PCR analyses in animals, yeasts, bacteria but rarely in plants  [52]. Our results based on geNorm were consistent with this couple of genes being very stable regarding gene expression in the analyzed samples. For anthesis, the two most stable genes were VvAIG1 and VvCCRP (Figure  4A ); in the case of fruit-setting these were VvUNP and VvCCRP (Figure  4B ); and for 6-8 mm berries, the most stable genes were VvUNP and VvAIG1 (Figure  4C ). Other genes considered in this work (EF, PP2A and UBQ10) were studied in other species of plants such as soybean  [53]  and Gossypium hirsutum  [23] , showing a high variability in their expression profile depending of the physiological condition, tissues and genotypes.
Other putative housekeeping genes reported and used in many works are the following: M, polyubiquitin (VvUBQ10, GenBank acc CB977307); N, plasma membrane intrinsic protein 2B (VvPIP2B, GenBank acc EC969993); and O, elongation factor 1-alpha (VvEF1-α, GenBank acc CB977561). Bars in the graphs correspond to standard error (SE) from three biological samples, assayed in duplicate. Different letters represent significant differences a t P < 0. 05 by LSD test. in the reference grape genome by TOPHAT  [54]  program. The differential expression test on seventy comparisons was implemented in the edgeR  [46]  software. Then, using in-house development scripts, we searched for genes that were classified as non-differentially expressed, and presented at least 100 reads in each sample/condition and a low variation index among conditions.
Primer design Gene-specific primers were designed using Primer Premier 5. 0 software (Premier Biosoft International, Palo Alto, CA) and synthesized by Alpha DNA (Montreal, Quebec, Canada). The nucleotide sequences were obtained from a private data-base maintained at http://vitisdb. cmm. uchile. cl/. In addition, three genes encoding a polyubiquitin (UBQ10), plasma membrane intrinsic protein 2B (PIP2B) and elongation factor 1-alpha (EF-1α) and their respective pairs of primers were selected from previously published reports  [28]  and evaluated as a way of comparison. Accession numbers, primer sequences, expected size of amplicons and melting temperature are provided in Table  5.
Data from qPCR was subjected to statistical analysis of variance, and means were separated by LSD test at 5% level of significance using Statgraphics Plus 5 (Manugistics Inc. , Rockville, MD). The RNA-Seq data used in this study is available at the NCBI's Sequence Read Achieve (http://www. ncbi. nlm. nih. gov/sra) with the SRA Study accession number SRX366617.
Methods PHIRE 9 was a collaborative action through the EUPHA within European member states. EUPHA 10 brings together national public health researchers through conferences and publishing the European Journal of Public Health, and also works on European health policy issues with other professional and non-governmental organizations, and within the EU Health Policy Forum. 11 n PHIRE, four partner organizations (in France, Malta, Slovakia, Lithuania) acted as regional coordinators, each linking with a further seven national public health organizations geographically. The partners created a data collection instrument, drawing on previous experience in developing national public health research profiles for Strengthening Engagement in Public Health Research (STEPS). 12 he definition of public health research (Box 1) was drawn from STEPS and its prior project SPHERE (Strengthening Public Health Research in Europe).
3, 13 The data included for each programme or call were: the name of the commissioning agency; description; funding and duration; and eligibility criteria. To enable comparable recording across a range of countries and with variability of national systems, in a limited period of time, the study focused on programs and calls that opened during 2010. A first version of the instrument was piloted in each of the countries of the regional coordinators, and revised through discussion. National public health associations were initially contacted through the Governing Council of EUPHA. The EUPHA president sent letters introducing the regional coordinators. For each country, the regional coordinators sought a person able and willing to provide the information, assistance in organizing the information and completing the forms.
uses a range of observational methods, including surveys, registers, data sets, case studies and statistical modelling, and draws on disciplines including epidemiology, sociology, psychology and economics, and interdisciplinary fields of environmental health, health promotion, disease prevention, health care management, health-services research and health-systems research. (BMC Public Health 2009;9:203. doi:10. 1186/1471-2458-9-203) ranged from aetiological to intervention studies, with some countries including health services research. More calls, one-third of the total, were identified for disease control. France has six calls specifically related to cancer (Supplementary Table  S4 ), mainly epidemiology, from the National Cancer Institute or the National League Against Cancer, and the UK had one on early detection, including health services.
But the classification offers some other entry points for public health research topics, such as 'tobacco', 'research design' and 'policy'. Analysis by 'code groups', which include 'prevention' and 'health services', presented as a 'kite diagram', has been used in making the case for more health research. 25 here is a major difficulty of bringing European data together. Only in France, the national research organization Inserm has a unit (IReSP) that collects public health research calls from both public and sectors. 26 Estonia and Slovenia each keep all their funded research on a single searchable database,  27, 28  while some other countries have semi-searchable databases. The EU's own system works at the level of programmes, but not realistically for public health keywords. There is an issue of scale (small countries identify projects, while large countries report programmes), and complexity-as more multi-disciplinary research makes it harder to track the public heath elements.
The Río de la Plata is one of the largest and most turbid estuaries of the world, carrying a total of 160 million tons y À 1 of suspended sediments. The knowledge of their spatial distribution and their scales of variability is fundamental for management and scientific reasons, but has been limited by the scarcity of observations. During 2009 and 2010, in-situ data (CTD and turbidity profiles, and water and bottom sediment samples) were collected at 26 sites during six repeated cruises and from three fixed instruments deployed in the frame of the FREPLATA/FFEM experiment. In this paper we complement the analysis of this in-situ data base with 10 years of daily intermediate resolution (1 km) MODIS-Aqua observations processed for surface suspended matter using the IFREMER algorithm for coastal turbid waters.
In this sense, between November 2009 and December 2010, the FREPLATA/FFEM (Environmental Protection of the Río de la Plata and its Maritime Front / French Fund for the Global Environment) Experiment was performed with the aim of gathering a new and rich in-situ data base of hydro-sedimentological variables to aid in the understanding and numerical modeling of the underlying processes  (Simionato et al. , 2011a; Simionato et al. , 2011b). The experiment included six cruises with 26 oceanographic stations each, where temperature, conductivity and turbidity profiles were measured and surface and bottom water samples were collected. Simultaneously, fixed instruments measuring waves, temperature, conductivity and turbidity were deployed at three sites of the estuary. One of these instruments also recorded meteorological variables.
Even though the efforts made during the FREPLATA/FFEM experiment represent a large improvement to the previous scarcity of data in the RDP, many more observations are necessary to study the high SM concentration variability observed in this system. In this sense, even though ocean color satellite observations are highly contaminated by the presence of clouds, they supply data with high temporal and spatial resolution, which can provide a synoptic view of the variables and which permit the analysis of their spatio-temporal variability  (Miller and McKee, 2004). The attractiveness of remote sensing as an information source has increased greatly over the last ten years as the new generation of satellite sensors has become available. In particular, more than 10 years of continuous observations at medium resolution (1 km) are available from the MODIS-Aqua (Moderate Resolution Imaging Spectroradiometer on board of the Aqua satellite) mission, becoming a unique source of data over a considerable large period of time.
(2011a Simionato et al. ( , 2011b)) , but the most important aspects will be summarized in what follows. The CTD was a "Seabird", model SBE-19; a "Campbell Scientific" OBS3 þ turbidimeter (NS 8226) was attached to it. The sampling frequency was 2 Hz what, at a downcast speed of 0. 3-0. 5 m s À 1 resulted on a vertical resolution of 4-7 data per meter. The CTD was operated in real time using an interface SBE NS 0128. The heart of the OBS3 þ sensor is a near infrared laser and photodiode for detecting the intensity of light scattered from suspended particles in water. The sensor was calibrated by the manufacturer. At every oceanographic station, water samples were collected with a pump with the aim of calibrating the conductivity (with an Autosal Guidline at the laboratory) and turbidity sensors.
The samples' turbidity was measured in-situ using a "HACH" 2100 P ISO portable turbidimeter with a range of 0-1000 NTU. Additionally, one liter samples of near surface water were gathered for the determination of the SM concentration at every station. These samples were analyzed by the Hydrographic Service of the Navy of Argentina. Once at the laboratory, the samples were filtered (diameter: 47 mm; pore: 0. 45 μm), dried at 40 1C for 12 h, kept in a drying cabinet until they cooled, and then weighed for the determination of the SM concentration. Several replicates at random of the samples were done for every cruise. The difference between replicates (accuracy) resulted less than 5%. This way, after samples' laboratory analysis and data processing, six temperature, salinity and turbidity profiles, so as surface SM and turbidity observations from the water samples were available at each of the 26 sites.
These data provide a synoptic view of the mentioned fields so as information about the seasonal variability. Simultaneously, fixed instruments measured high temporal resolution time series of diverse variables in three sites; two of them correspond to the Pilote Norden and Torre Oyarvide towers and the third was close to Pontón Recalada with an oceanographic buoy. The locations of the instruments were indicated as squares in Fig. 1. The instruments fixed to Pilote Norden and Torre Oyarvide recorded pressure (for waves), temperature, conductivity and turbidity every 15 min during approximately 1 and 2 months, respectively. In Pontón Recalada hourly meteorological observations and water temperature, conductivity and turbidity data were gathered during one year. At all the three stations an "NKE" SMATCH TT multiparameter probe was used; it included a "Seapoint" turbidimeter.
MODIS-aqua suspended matter data Daily standard remote-sensing reflectances of MODIS-Aqua have been used in this study. The MODIS Level-2 reflectance products have been downloaded in near real time from the OceanColor/GSFC (Goddard Space Flight Centre, http://modis. gsfc. nasa. gov). The temporal resolution is of one day, the horizontal resolution of 1 km and data collection spans the period August 8th 2002 to August 23th 2012. The normalized remote-sensing reflectances were processed by IFREMER for estimating Chlorophyll-a (Chl-a) concentration and non-algal suspended matter (SM) using algorithms and methods established for the Bay of Biscay and the English Channel  (Gohin et al. , 2005 , Gohin, 2011)  and used after validation on the European North-West shelf, including the turbid coastal waters of the United Kingdom  (Sykes and Barciela, 2012).
, 2011). On average, over the area considered, 28% of the days have a valid observation. To analyze the remote data quality, Fig. 3a  shows a scatter plot of the remote (MODIS-Aqua) vs. in-situ SM observations. This figure was built by assigning to every SM in-situ observation derived from the water samples collected during the six FRE-PLATA/FFEM Experiment quasi-synoptic cruises, an average of the available MODIS-Aqua data from one day before to one day after the period of each cruise. This way the comparison has several limitations that must be taken into account when analyzing the plot. The most important of them is the fact that the MODIS-Aqua mean and the in-situ data do not necessarily represent to the same instant; this way, there is a potential effect of tides and winds on in-situ vs.