Automatic Identification of Defects on Eggshell Through a Multispectral Vision System

The objective of this research was to develop an off-line artificial vision system to automatically detect defective eggshells, i.e., dirty or cracked eggshells, by employing multispectral images with the final purpose to adapt the system to an on-line grading machine. In particular, this work was focused to study the feasibility of identifying organic stains on brown eggshells (dirty eggshell), caused by blood, feathers, feces, etc., from natural stains, caused by deposits of pigments on the outer layer of clean eggshells. During the analysis a total of 384 eggs were evaluated (clean: 148, dirty: 236). Dirty samples were evaluated visually in order to classify them according to the kind of defect (blood, feathers, and white, clear or dark feces), and clean eggshells were classified on the basis of the colour of the natural stains (clear or dark). For each sample digital images were acquired by employing a Charged Coupled Device (CCD) camera endowed with 15 monochromatic filters (440-940 nm). A Matlab® function was developed in order to automate the process and analyze images, with the aim to classify samples as clean or dirty. The program was constituted by three major steps: first, the research of an opportune combination of monochromatic images in order to isolate the eggshell from the background; second, the detection of the dirt stains; third, the classification of the images samples into the dirty or clean group on the basis of geometric characteristics of the stains (area in pixel). The proposed classification algorithm was able to correctly classify near 98% of the samples with a very low processing time (0.05s). The robustness of the proposed classification was observed applying an external validation to a second set of samples (n = 178), obtaining similar percentage of correctly classified samples (97%)

PML isoforms IV and V contribute to adenovirus-mediated oncogenic transformation by functionally inhibiting the tumor-suppressor p53

Although modulation of the cellular tumor-suppressor p53 is considered to have the major role in E1A/E1B-55K-mediated tumorigenesis, other promyelocytic leukemia nuclear body (PML-NB)/PML oncogenic domain (POD)-associated factors including SUMO, Mre11, Daxx, as well as the integrity of these nuclear bodies contribute to the transformation process. However, the biochemical consequences and oncogenic alterations of PML-associated E1B-55K by SUMO-dependent PML-IV and PML-V interaction have so far remained elusive. We performed mutational analysis to define a PML interaction motif within the E1B-55K polypeptide. Our results showed that E1B-55K/PML binding is not required for p53, Mre11 and Daxx interaction. We also observed that E1B-55K lacking subnuclear PML localization because of either PML-IV or PML-V-binding deficiency was no longer capable of mediating E1B-55K-dependent SUMOylation of p53, inhibition of p53-mediated transactivation or efficiently transforming primary rodent cells. These results together with the observation that E1B-55K-dependent SUMOylation of p53 is required for efficient cell transformation, provides evidence for the idea that the SUMO ligase activity of the E1B-55K viral oncoprotein is intimately linked to its growth-promoting oncogenic activities