10 research outputs found

    Assessment of the visual quality of ornamental plants: Comparison of three methodologies in the case of the rosebush

    Get PDF
    The quality of ornamental plants can be appraised with several types of criteria: tolerance to biotic and abiotic stresses, development potentialities and aesthetics. This last criterion, aesthetic quality, is specific to ornamental plants and objective measurements are required. Three methodologies for measuring aesthetic quality have been proposed. The first involves classical measurements of morphological features, such as flower number and diameter or leaf size. The second is based on sensory methods recently adapted to ornamental plants. The third, used by the International Union for the Protection of New Varieties of Plants (UPOV) for distinctness, uniformity and stability (DUS) tests, is based on morphological characteristics calibrated on specific reference varieties. The aim of this work was to compare these three methodologies for assessing some flowering and foliage characteristics of rosebushes. Six plants from 10 rose varieties identified by UPOV as reference varieties were cultivated for two years in a greenhouse and outdoors in Angers, France. They were measured and photographed weekly during flowering. Photographs of the plants in full bloom were submitted to a panel of judges for sensory assessment. The results of the three assessment methodologies were compared. Sensory and morphometric measurements were highly correlated and sensory measurements confirmed UPOV scales, whereas some morphometric measures diverged slightly from UPOV scales. We discuss the advantages, disadvantages and complementarity of these three methodologies

    Phenoplant: a web resource for the exploration of large chlorophyll fluorescence image datasets

    Get PDF
    Background Image analysis is increasingly used in plant phenotyping. Among the various imaging techniques that can be used in plant phenotyping, chlorophyll fluorescence imaging allows imaging of the impact of biotic or abiotic stresses on leaves. Numerous chlorophyll fluorescence parameters may be measured or calculated, but only a few can produce a contrast in a given condition. Therefore, automated procedures that help screening chlorophyll fluorescence image datasets are needed, especially in the perspective of high-throughput plant phenotyping. Results We developed an automatic procedure aiming at facilitating the identification of chlorophyll fluorescence parameters impacted on leaves by a stress. First, for each chlorophyll fluorescence parameter, the procedure provides an overview of the data by automatically creating contact sheets of images and/or histograms. Such contact sheets enable a fast comparison of the impact on leaves of various treatments, or of the contrast dynamics during the experiments. Second, based on the global intensity of each chlorophyll fluorescence parameter, the procedure automatically produces radial plots and box plots allowing the user to identify chlorophyll fluorescence parameters that discriminate between treatments. Moreover, basic statistical analysis is automatically generated. Third, for each chlorophyll fluorescence parameter the procedure automatically performs a clustering analysis based on the histograms. This analysis clusters images of plants according to their health status. We applied this procedure to monitor the impact of the inoculation of the root parasitic plant Phelipanche ramosa on Arabidopsis thaliana ecotypes Col-0 and Ler. Conclusions Using this automatic procedure, we identified eight chlorophyll fluorescence parameters discriminating between the two ecotypes of A. thaliana, and five impacted by the infection of Arabidopsis thaliana by P. ramosa. More generally, this procedure may help to identify chlorophyll fluorescence parameters impacted by various types of stresses. We implemented this procedure at http://www.phenoplant.org webcite freely accessible to users of the plant phenotyping community

    Minimum information about a protein affinity reagent (MIAPAR)

    Get PDF
    This is a proposal developed within the community as an important first step in formalizing standards in reporting the production and properties of protein binding reagents, such as antibodies, developed and sold for the identification and detection of specific proteins present in biological samples. It defines a checklist of required information, intended for use by producers of affinity reagents, qualitycontrol laboratories, users and databases. We envision that both commercial and freely available affinity reagents, as well as published studies using these reagents, could include a MIAPAR-compliant document describing the product’s properties with every available binding partner. This would enable the user or reader to make a fully informed evaluation of the validity of conclusions drawn using this reagent

    A Community Standard Format for the Representation of Protein Affinity Reagents*

    No full text
    Protein affinity reagents (PARs), most commonly antibodies, are essential reagents for protein characterization in basic research, biotechnology, and diagnostics as well as the fastest growing class of therapeutics. Large numbers of PARs are available commercially; however, their quality is often uncertain. In addition, currently available PARs cover only a fraction of the human proteome, and their cost is prohibitive for proteome scale applications. This situation has triggered several initiatives involving large scale generation and validation of antibodies, for example the Swedish Human Protein Atlas and the German Antibody Factory. Antibodies targeting specific subproteomes are being pursued by members of Human Proteome Organisation (plasma and liver proteome projects) and the United States National Cancer Institute (cancer-associated antigens). ProteomeBinders, a European consortium, aims to set up a resource of consistently quality-controlled protein-binding reagents for the whole human proteome. An ultimate PAR database resource would allow consumers to visit one on-line warehouse and find all available affinity reagents from different providers together with documentation that facilitates easy comparison of their cost and quality. However, in contrast to, for example, nucleotide databases among which data are synchronized between the major data providers, current PAR producers, quality control centers, and commercial companies all use incompatible formats, hindering data exchange. Here we propose Proteomics Standards Initiative (PSI)-PAR as a global community standard format for the representation and exchange of protein affinity reagent data. The PSI-PAR format is maintained by the Human Proteome Organisation PSI and was developed within the context of ProteomeBinders by building on a mature proteomics standard format, PSI-molecular interaction, which is a widely accepted and established community standard for molecular interaction data. Further information and documentation are available on the PSI-PAR web site

    Minimum information about a bioactive entity (MIABE)

    Get PDF
    Bioactive molecules such as drugs, pesticides and food additives are produced in large numbers by many commercial and academic groups around the world. Enormous quantities of data are generated on the biological properties and quality of these molecules. Access to such data — both on licensed and commercially available compounds, and also on those that fail during development — is crucial for understanding how improved molecules could be developed. For example, computational analysis of aggregated data on molecules that are investigated in drug discovery programmes has led to a greater understanding of the properties of successful drugs. However, the information required to perform these analyses is rarely published, and when it is made available it is often missing crucial data or is in a format that is inappropriate for efficient data-mining. Here, we propose a solution: the definition of reporting guidelines for bioactive entities — the Minimum Information About a Bioactive Entity (MIABE) — which has been developed by representatives of pharmaceutical companies, data resource providers and academic groups
    corecore