Optimistic concurrency control is widely studied in the literature due to the properties of non-blocking and deadlock free execution especially in the domain of real-time systems. In this paper we review the substantial research of optimistic concurrency control protocols. We characterize them into four categories and explore their properties. Then we investigate the general concepts and properties related to Optimistic concurrency control Finally, we demonstrate a comparison table between the varieties of these

protocols.

 The tetracycline promoter (tet) has been manipulated in several systems as an inducible regulator of gene expression. In this study the effect of doxycycline on C. glabrata viability has been examined in water and according to CLSI standard method. In addition, flow cytometry analysis was performed to detect SLT2 protein activation by sub-inhibitory concentrations of doxycycline. Our findings show that 100 µg/ml of doxycycline is the maximum tolerated dosage in media; however, C. glabrata was sensitive to 40-50µg/ml in water. 50 µg/ml of doxycycline induced SLT2p phosphorylation and our results support the idea of involvement of doxycycline in alteration of C. glabrata oxidative status. These results should be considered when the tet promoter system is used in yeast molecular studies

Malignant melanoma is one of the most rapidly increasing cancers globally and it is the most dangerous form of human skin cancer. Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma. Early detection of melanoma can be helpful and usually curable. Due to the difficulty for dermatologists in the interpretation of dermoscopy images, Computer Aided Diagnosis systems can be very helpful to facilitate the early detection. The automated detection of the lesion borders is one of the most important steps in dermoscopic image analysis. In this paper, we present a fully automated method for melanoma border detection using image processing techniques. The hair and several noises aredetected and removed by applying a bank of directional filters and Image Inpainting method respectively. A hybrid method is developed by combining Particle Swarm Optimization and Markov Random Field methods, in order to delineate the border of the lesion area in the images. The method was tested on a dataset of 200 dermoscopic images, and the experimental results show that our method is superior in terms of the accuracy of drawing the lesion borders compared to alternative methods.