dataesr/scientific-paragraphs-categorization huggingface dataset:
g. parent-child or parent-parent-child)  [33]. The failed SNPs were not used in the mapping process. The RosBREED cherry 6K SNP array v1 markers used in this work were deposited in NCBI's dbSNP repository available at www. ncbi. nlm. nih. gov/projects/SNP [34]  and each SNP was given a unique accession number that starts with the prefix ss (SNPs NCBI ss# database names). More information associated with these SNPs is available at the Genome Database for Rosaceae (GDR; www. rosaceae. org [35] ). The location of the markers on the physical map of the peach genome v1. 0 as well as their positions on the genetic maps of BT, K, BT6K, L, R and R6L are available in Table  S1. The MAF (minor allele frequency) distribution of SNPs heterozygous in each of the four mapping parents ('Black Tartarian', 'Kordia', 'Regina' and 'Lapins') was determined and then compared with a germplasm collection of 269 sweet cherry selections used by Peace et al.
Acknowledgments The authors thank the technical team of  INRA   UR419 ,  Jacques Joly  and  Lydie Fouilhaux , for producing the hybrids and the  Experimental Unit of INRA  for growing the trees. We would like also to thank  Audrey Sebolt  from  Michigan State University  for her assistance concerning the RosBREED cherry 6K SNP array v1 analyses; and  Fernanda Rodriguez ,  Nicolas Bricen  ˜o,  Ingrid Araya  for their technical assistance in leaf sampling and DNA extraction and quantification at  Universidad Andres Bello, Chile. The authors would also like to thank  Cameron Peace  from  Washington State University  for his comments and advice during the revision of the manuscript.
This work was funded by  07CN13PBT-167   INNOVA Chile CORFO  project, and by  INRA's Department of ''Genetic and Plant Breeding '' and the  Conseil Regional d'Aquitaine 2011  project ''convention  2011-12 01 00122000568 '' for analyses performed in France. Additional funding was provided by  Universidad Andres Bello   DI-78-12/I project , the  USDA's National Institute of Food and Agriculture -Specialty Crop Research Initiative  project '' RosBREED: Enabling markerassisted breeding in Rosaceae '' ( 2009-51181-05808 ),  CONICYT  Fondecyt/regular number  1120261  and  CONICYT  Fondecyt/regular number  1121021. CK holds a  CONICYT doctoral fellowship  ( 21120115  and  24121484 ). JC is holder of a postdoctoral fellowship from the  ''Programa de Formacion Postdoctoral de Personal Investigador'' of the ''Fundacion Seneca-Agencia de Ciencia y Tecnologı ´a de la Region de Murcia'', Spain.
The study demonstrated that optimal results could be achieved by the majority of laboratories. Specific protocols showed evenly good performances and can therefore be recommended to all expert laboratories. However, other methods showed uneven performances suggesting the need for improved optimization and standardization of these protocols. result whereas false-negative/-positive results were not scored. Equivocal or borderline results were not counted as molecular diagnostic methods should always provide a clear positive or negative result. Results were classified as: A total of 39 datasets were received including 5 double sets from laboratories using 2 methods (lab #6, 7, 21, 27 and 28) and 2 triple sets from lab #5 and #14. Methods used by the same laboratory could differ from the type of technique, the protocol used for a specific technique or the type of instrument used for a specific protocol.
Performances varied among laboratories and scores ranged from 7 to the maximum value of 14. Optimal results were reported by 64% (n = 25) of the analyses; 21% (n = 8) of the analyses achieved acceptable results due to the inability to detect one positive sample, and 15% (n = 6) revealed several false negative and/or one or more false positive results indicating that there is still need for improvement (Table  2  and 3 ). Several techniques were performed by the participating laboratories for detection of RVFV RNA. Among the 39 datasets received, we listed the use of real-time reverse transcription (qRT)-PCR only (n = 32/82%), RT-nested PCR only (n = 3/8%), RTnested PCR and real-time RT-PCR combined (n = 2/5%), RT-Loop-mediated isothermal amplification (RT-LAMP) (n = 1/ 2,5%) and recombinase polymerase amplification (RPA) (n = 1/ 2,5%) (Table  2  and 3 ).
, 2003  [34]. The sole set of results obtained by RT-LAMP adopted the protocol published by Le Roux et al. in 2009  [26]. The only RPA set of results was obtained by applying the protocol from Euler et al. published in 2012  [27]  with RPA reagents provided by TwistDx. The diagnostic method employed for each set of results        2  and 3 as well as the references corresponding to each protocol. Performance comparisons between each type of technique are limited as some methods are not sufficiently represented (e. g. n = 1 for RPA and RT-LAMP). For this reason a statistical analysis could not reach any significant conclusion. Nevertheless some general observations can be clearly delineated from the results. The 32 datasets obtained by using qRT-PCR only, were all highly scored with 13 or 14 points.
Nevertheless it is interesting to notice that the 2 datasets obtained with qRT-PCR combined with nested RT-PCR scored 11 and 12 points demonstrating lower performances than qRT-PCR alone. Laboratory #22 lost 2 points due to 2 false positives generated by nested RT-PCR and thus would have demonstrated optimal performances if considering the results of the qRT-PCR only. The maximum score of 14 was also obtained by the only set of results reporting the use of RPA technology. On the other hand, none of the methods involving nested RT-PCR achieved an optimal performance and the corresponding scores ranged from 7 to 13. The dataset obtained with RT-LAMP showed a score of 11 mostly because of difficulties in detecting the less concentrated samples. The sensitivity of the different diagnostic methods can be assessed by comparing the testing results of the serial dilutions of RVFV-Kenya/2007 (samples #2, #9, #12, #4, and #14) and RVFV-Egypt/1994 (samples #5 and #13).
The rest of false negatives results were distributed equivalently among the other samples, except for sample #2 (highly concentrated dilution of RVFV-Kenya/2007) and sample #6 (RVFV-Zimbabwe/1979) which presented 100% of correct results. Furthermore, it is interesting to note that several datasets (#9, 15, 21b and 29) revealed false negative results at lower dilutions of a serial dilution but reported a positive correct result at a higher dilution for the same strain. Such false negative results cannot be attributed to sensitivity nor strain specificity issues but rather to the lack of reproducibility and consistency of the employed test procedure. This is a clear indication of the need to improve test procedures by applying standardized protocols and accurate testing procedures. When comparing test results obtained for the different RVFV strains, we observed even performances indicating an overall good specificity as the different techniques can detect several strain types.
However, some individual set of results indicated a potential lack of specificity concerning methods such as the qRT-PCR protocol from Garcia et al. [23]  which was not able to detect both dilutions of the RVFV-Egypt/1994 strain, and the nested RT-PCR protocol of Sanchez-Seco et al. [34]  which was not able to detect the South-Africa/1981 strain and the Central-African-Republic/1985 strain (Table  3 ). Nonetheless this information should be interpreted with precaution as only one laboratory reported the use of each of these protocols. We can also have indications on the specificity of each diagnostic method by analyzing the testing results of the two negative controls (samples #7 and #8). Altogether, 8% of the datasets (3 out of 39) reported false positives. Two of these 3 datasets originated from nested RT-PCR techniques, one reported to use of the protocol of Sall et al.
[22]  and the other the protocol of Sanchez-Seco et al. [34]. The third false positive was engendered by qRT-PCR and indicated a low viral load (Table  3 ). In order to evaluate practice in viral load determination, participants were requested to provide the number of copies of RVFV genome detected in positive samples. Over 73% of the laboratories (22 out of 30) reported quantitative results and are reported in bold in Table  3  and 3  although the numeric values are not shown. The majority of these laboratories (14 out of 22) gave their results as cycle threshold (Ct) values providing insufficient data to estimate accurately viral load in the samples. Interestingly, datasets providing no information on viral load originated not only from techniques unable of providing quantitative results (RPA, RT-LAMP and nested RT-PCR) but also from real time-based procedures (6 out of 32) which are capable of determining quantitative values (Table  2  and 3 ).
All datasets obtained by qRT-PCR only were scored with 13 or 14 points indicating an evenly high performance of all qRT-PCR procedures performed by the different laboratories. Protocols from Drosten et al, 2002  [24] , Bird et al. 2007  [25] , Weidmann et al 2008  [31]  as well as all in-house qRT-PCR protocols (dataset #6b, #10 and #24) have demonstrated the capacity of providing optimal performances indicating a good specificity and sensitivity for these techniques. The sets of results obtained by applying the qRT-PCR protocols of Mweango et al. 2012  [35] ,  Garcia et al. 2001 [23]  and Busquets et al. 2010  [32]  did not achieve optimal performances (scores 13, 11 and 13 respectively) but these techniques are not sufficiently represented to conclude on their overall performances. Information on the viral load of RVFV in human samples can be very useful to monitor the progress of clinical manifestations and to study the pathogenesis of RVFV.
The dataset #14c obtained a score of 13 with the protocol of  Sall et al. 2002 [22]  because it could not detect the highest dilution of the RVFV-Egypt/1994 strain indicating a slightly low sensitivity just as observed for some of the qRT-PCR methods. Nevertheless other datasets referring to nested RT-PCR (#9 and #22) also reported false positive results indicating a lack of specificity of these procedures with both nested RT-PCR protocols  [22, 34]. It is interesting to notice the appearance of newly developed techniques which are suitable for rapid field diagnostics such as RT-LAMP developed in 2009  [26]  and RPA technology developed in 2012  [27]. No general conclusion can be achieved concerning the performances of these two techniques as they both have been performed by only one laboratory.
However RPA has shown optimal results for this EQA demonstrating equivalent sensitivity and specificity to the qRT-PCR techniques (dataset #27b). On the other hand, RT-LAMP results indicated difficulties in detecting RVFV genome in the less concentrated samples of the panel (sample #4, #13 and #14). These results suggest some limitations in test sensitivity. However, very high test sensitivity is not essential for field diagnostics in an outbreak situation where most diagnosed patients are in the acute phase of the disease and are expected to present a high viremia. Three laboratories have provided different sets of results which referred to the same technique and protocol but using different instruments (datasets #5b/c, #14a/b and #28 a/b). These datasets provided all optimal results by using two different instruments except for dataset #14 which reported a slightly lower sensitivity using the SmartCycler System from Cepheid (#14b, 13 points) compared to the 7500 Real-Time PCR System from Applied Biosystems (#14a, 14 points).
The  European Center for Disease prevention and Control (ENIVD/ECDC)  for financial support under the contract no. ECDC/2008/011. Part of this work has been facilitated through the  International Network for Capacity Building  for the Control of  Emerging Viral Vector Borne Zoonotic Diseases (Arbo-Zoonet ) supported by the  European Union  under grant agreement no. 211757. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
What is the evolutionary potential of selected species to adapt to short and long-term environmental changes as imposed by human impacts on natural systems? The incorporation of genomic and transcriptomic techniques in the analysis of marine ecosystems and species can help us determine this  (Procaccini et al. , 2007; Reusch and Wood, 2007). Most of the more advanced-omics techniques have been developed in laboratory model-species, such as Arabidopsis or Oryza, but some of them are also applicable in species for which genomic resources are scarce or absent, such as most of seagrass species. In the last few years, seagrass genomic and transcriptomic resources are increasing, particularly in two species of the genus Zostera, Zostera marina, and Z. noltii, and on P. oceanica. An on line EST (Expressed Sequence Tags) database, Dr.
marina and P. oceanica  (Oetjen and Reusch, 2007; D'Esposito et al. , in press ) and have been utilized, together with SNPs markers, in a genome-scan analysis on Z. marina  (Oetjen et al. , 2010). Boosting genomics information in EST database makes, from now, proteomic analyses more attractive for P. oceanica than in the past, because protein sequence analysis and identification are less challenging. Proteomics is a promising powerful tool to compare quantitative/qualitative differences in thousands of proteins in P. oceanica from meadows living in different environments. Hence, by identifying the expression of different proteins under various conditions, we might validate these proteins as early biomarkers for ecophysiology assessment. On the other hand, the various metabolic pathways which are utilized under different conditions could represent a starting point to clarify how P.
oceanica is able to adapt. To express its full potential, proteomics must rely on samples of high protein quality. Consequently, the newer methods of extraction, separation, and analysis of the entire proteome from a specific tissue, or from organelles that are now evolving in many plants, must also be implemented for seagrasses at local and large scales. The protein expression approach and the bottom-up experimental design together with the high-throughput technologies for mass spectrometry promise a large amount of empirical information on the seagrass proteome in the coming years. This large amount of information should be added to relevant seagrass databases in order to facilitate the organization of data to generate testable hypothesis. The application of new technique(s) combining twoor one-dimensional (1D) SDS-PAGE with a high-mass-accuracy LC-ESI-MS and LC-SACI-MS and MS/MS to sequence identification approaches has demonstrated an increase in the confidence of results  (Finiguerra et al.
, 2010). This can provide a highthroughput system to achieve the goal of sequencing complete proteomes from seagrass organs and tissues. By databases such as EST, Transcriptomics, and Genomics, the genomic data from seagrasses can be interrelated with the emerging protein sequences and metabolic data as well as with environmental information. How are seagrasses able to biochemically survive a marine life style? Since the proteome of each living cell is dynamic, proteomics allows investigators to clarify if, and to what extent, various pathways are utilized under varying conditions and triggered by the action of the environment on the system, and the relative protein level response times. Previously, two-dimensional gel-based proteomic studies on Posidonia meadows acclimated to different light conditions revealed physiological pathways involved in the acclimation of seagrasses to low-light, evidenced by Rubisco downregulation; in contrast, enzymes involved in carbohydrate cleavage (1-fructose-bisphosphate aldolase, nucleoside diphosphate kinase, and beta-amylase) were up-regulated  (Mazzuca et al.
The marine lab offers direct access to the sea, and facilitates investigations using diving, boats, and laboratories. Since 1970, time series of physical, chemical, and biological data (sampling at sea with automated systems and sensors deployed in the Bay, as well as in situ experiments) have been recorded. In front of the lab, P. oceanica (L. ) Delile is the dominant ecosystem going from the surface to a lower limit that reaches 37 m. A large collection of data focused on P. oceanica ecosystem diversity and functioning has been collected over the last 40 years. As a result, the seasonal and inter-annual dynamics of the major primary producers relating to the ambient parameters (temperature, winds, nutrient concentrations) are well known in the site  (Bay, 1984; Lepoint et al. , 2002; Gobert et al.
WHAT TIMING, WHAT METHODOLOGIES AND TECHNOLOGIES FOR COOPERATIVE SAMPLES COLLECTION, IN SITU DEPLOYMENT OF EQUIPMENT, DATA COLLECTION, AND ANALYSIS As pointed out above, there are several ways to estimate the metabolic processes of seagrasses, each having its advantages, but measuring with different approaches. In order to compare such data, sampled with different methods, it is important to perform simultaneous field methods calibrations. We selected the meadow along a deep gradient and fixed daytimes corresponding to supersaturating and limiting irradiances as extreme conditions. Between these, many intermediate times were considered. Timing between the underwater in situ field analyses, the sampling of biological material and the processing of tissue for molecular analyses, which is typically done by fixing tissues in suitable buffers or by freezing them in liquid nitrogen, is the real challenge; the shorter the time between these events, the greater the confidence in the results from different specialties.
Incorporation of genomics tools To incorporate gene expression analysis in the study of the main metabolic pathways involved in carbon budget, two different approaches were taken. First, total transcriptome profiling was obtained using an Illumina next generation sequencing platform (Illumina GAIIx) available at the Genomics Research Centre (CRA_GPG, Fiorenzuola D'Arda, PC, Italy). High quality total RNA was extracted from pulled leaves from four individual shoots, two of which were collected at 5 m depth (12. 00 and 18. 30) and the other two at 20 m depth (12. 00 and 18. 30). Four complementary DNA (cDNA) libraries were prepared and run in a single Illumina GAIIx plate. Fragments were assembled in longer consensus sequences (contigs) and the following bioinformatic analysis allowed the identification of expressed genes through the annotation of contigs against public databases.
All ESTs obtained will be stored in a new database, which will be made available to the scientific community. ESTs from the four samples were compared in order to find differentially expressed genes at the two depths and in two different time points. The set of differentially expressed genes was related to the physiological performances of the plant in the different conditions, and will serve as the basis for selecting sets of environmental responsive genes. Second, expression levels of a set of genes encoding for molecular components of the photosynthetic and respiration apparatus were evaluated by RT-qPCR. Candidate genes have been selected according to their role in the different phases and in the different compartments of both processes (Table  2 ). For photosynthesis, we selected as genes of interest two structural components of Photosystem I (PSI), three genes encoding for subunits of PSII, two genes encoding for antenna proteins for each of the two Light-Harvesting complexes (LHCI and LHCII), one component of the chloroplast electron transport chain (Ferredoxin), and the gene encoding for RuBisCO small subunit.
RT-qPCR reactions were performed in MicroAmp Optical 384-Well reaction plate (Applied Biosystem) with Optical Adhesive Covers (Applied Biosystem) in a Viia7 Real Time PCR System (Applied Biosystem) using Sybr Green as fluorescent detection chemistry. RT-qPCR amplifications were conducted in 10 µl reaction volumes containing 5 µl of Fast Start SYBR Green Master Mix (Roche), 1 µl of cDNA template and 0. 7 pmol µl -1 of each primer. Thermal profile was obtained as follows: 95˚C for 10 min, 40 times 95˚C for 15 s and 60˚C for 1 min, 72˚C for 5 min. For determining the specificity of the reaction, the melting curve of each amplicon from 60 to 95˚C was also detected. The expression levels of each target gene were determined with REST tool (Relative expression software tool)  (Pfaffl et al. , 2002).
Protein samples from all samples were processed on 1D SDS-PAGE; the Laemmli buffer system was used to cast a 6% stacking gel and 12. 5% resolving gel. After denaturation at 100˚C for 3 min, proteins were resolved at constant 200 V in a Bio-Rad mini Protean II apparatus. Peptide bands were quantified using Quantity One software (Bio-Rad). For each lane, area, and density of bands were calculated. Band volume was the product of band area and density. After background subtraction, band volume was normalized as the percentage of the total volume of protein bands on the same lane. The normalized volume (NV) of single band on the multiple gels from single depth and among samples was calculated, which was reproducible with 90% accuracy.
Orbitrap-LC-MS/MS and protein identification. Gel slides from each SDS-PAGE were cut in six slices and digested enzymatically with trypsin. Tryptic peptides were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a high resolution LTQ-Orbitrap spectrometer (Thermo). Chromatography separations were conducted on a Waters XBridge C18 column (300 µm I. D. × 100 mm length and 3. 5 µm particle size), using a linear gradient from 5 to 90% ACN, containing 0. 1% formic acid with a flow of 4 µl min -1 , including the regeneration step, one run lasted 70 min. Acquisitions were performed in the data-dependent MS/MS scanning mode (full MS scan range of 250-1800 m z -1 followed by full MS/MS scan for the most intense ion from the MS scan). This yielded de novo protein sequences suitable for database searching.
At first, peptide sequences generated by mass spectrometry were searched using GPM software (Global Proteome Machine) against plant databases. Peptide sequences, that were not identify with the method above, were further searched to GPM website using X!Tandem algorithm against the local database sequences building with all available P. oceanica and Z. marina sequences found in NCBI, Uniprot, and DrZompo databases (see  Dattolo et al. , submitted).
1-DE Free-label approach and relative quantification by spectral count. The Figure  4A  shows the 1-DE-SDS separation of samples and the gel slices at ranges of molecular weight that were compared to detect differentially expressed proteins. The samples represent total protein extracts from P. oceanica adult leaves at different depths along the daily cycle. The differentially expression of proteins were evaluated by the labeling-free approach  (Zhang and Wang, 2009). The workflow employed is described in the Figure  4B. After the proteins were digested with trypsin, the peptides obtained were analyzed Orbitrap-LC-MS/MS in singly charged ion production mode, and the peptide fingerprint was acquired using a high-mass-accuracy q-TOF instrument. In the spectral counting approach, relative protein quantification is achieved by comparing the number of identified MS/MS spectra from the same protein in each of the multiple LC-MS/MS datasets.
(in press). The EST-msat loci were assembled in four multiplex PCR reactions, capillary electrophoresis was performed in a Applied Biosystems 3730 DNA Analyzer and electropherograms were automatically scored using the software Peak Scanner (ABI). Search for outliers was performed comparing the two depths and other populations at variable distance from the study site. Ad hoc software was utilized and only loci positive to different statistical approaches were retained as real. Function of EST regions linked to outlier loci were evaluated in order to assess if depth has an effect on selecting genes related to carbon budget.