Cloning of PCP1, a member of a family of pollen coat protein (PCP) genes from Brassica oleracea encoding novel cysteine-rich proteins involved in pollen-stigma interactions.

The pollen coatings of both Brassica oleracea and Brassica napus contain a small family of basic 6-8 kDa proteins which are released on to the stigmatic surface on pollination. Following partial amino-acid sequencing of one of these pollen coat proteins (PCPs), PCR primers were constructed to isolate the PCP sequence from anther mRNA using RT-PCR. A cDNA was obtained which, in Northern hybridization experiments, revealed a characteristic pattern of expression during late stages of anther development. Interestingly, in situ hybridization revealed expression of this sequence to be confined to the cytoplasm of the trinucleate pollen grains: no signal was detected in the tapetum. Southern hybridization experiments have shown the gene (PCP1) to be a member of a large family of between 30 and 40 PCP genes in the genome of Brassica oleracea. Surprisingly, RFLP experiments showed reduced copy number (one to two copies) in some of the F2 segregants, perhaps resulting from the clustering of PCP sequences. PCP1 contains a single intron and encodes a small, basic peptide 83 amino acids in length featuring a hydrophobic signal peptide sequence separated from the more hydrophilic, cysteine-rich mature protein. The central part and C-terminal region of the peptide contain a characteristic and invariant pattern of eight cysteines which show clear homology with a number of other anther-specific genes; the remainder of the sequence shows little similarity to other sequences on the data bases. The product of PCP1 is a member of a large family of similar proteins, some of which have been demonstrated to bind specifically to S-locus glycoproteins, but does not appear to be genetically linked to the S-locus

SVG-to-RDF Image Semantization

International audienceThe goal of this work is to provide an original (semi-automatic) annotation framework titled SVG-to-RDF whichconverts a collection of raw Scalable vector graphic (SVG) images into a searchable semantic-based RDF graph structure that encodes relevant features and contents. Using a dedicated knowledge base, SVG-to-RDF offers the user possible semantic annotations for each geometric object in the image, based on a combination of shape, color, and position similarity measures. Our method presents several advantages, namely i) achieving complete semantization of image content, ii) allowing semantic-based data search and processing using standard RDF technologies, iii) while being compliant with Web standards (i.e., SVG and RDF) in displaying images and annotation results in any standard Web browser, as well as iv) coping with different application domains. Our solution is of linear complexity in the size of the image and knowledge base structures used. Using our prototype SVG2RDF, several experiments have been conducted on a set of panoramic dental x-ray images to underline our approach’s effectiveness, and its applicability to different application domains