Background: Cytology is one of the important diagnostic tests done on effusion fluid. It can detect malignant cells in up to 60% of malignant cases. The most important benign cell present in these effusions is the mesothelial cell. Mesothelial atypia can be striking andmay simulate metastatic carcinoma. Many clinical conditions may produce such a reactive atypical cells as in anemia,SLE, liver cirrhosis and many other conditions. Recently many studies showed the value of computerized image analysis in differentiating atypical cells from malignant adenocarcinoma cells in effusion smears. Other studies support the reliability of the quantitative analysisand morphometric features and proved that they are objective prognostic indices. Methods: Sixty three cases of pleural and peritoneal smears, previously reported as benign (19) cases, malignant (21) cases or atypical (23) cases, were retrieved from the files. In each of these smears; nuclear area, perimeter, and roundness coefficient of 80-100 cell were determined at x400 magnification by the use of image analysis system. Statistical analysis was performed using analysis of variance and Tukey's HSD test. Results: The mean values of nuclear roundness, nuclear perimeter and nuclear area vary between the three groups (benign, atypical and malignant cells) by using analysis of variance (p > 0.01). The value of nuclear roundness, perimeter and area did not differ significantly between benign and atypical cells (Tukey’s test: p<0.01). On the other hand, the value of nuclear roundness, perimeter and area showed a significant difference between malignant and atypical cells(Tukey's test: p> 0.01). Conclusion: In conclusion, our data suggest that cytomorphometry performed on effusion smear cells may provide important information for the differentiation of atypical cells from malignant cells, in which the values of atypical cells are closer to those of benign cells during the examination of pleural and peritoneal smears by the use of image analysis system
Soil fertility is a crucial factor in measuring soil quality, it indicates the extent to which soil can support plant life. Soil fertility is measured by the amount of macro and micronutrients, pH, etc. Soil nutrients are depleted after each harvest and therefore must be added. To maintain soil nutrient levels, fertilizer is added to the soil. Adding fertilizer in the precise amount is a matter of great importance because excess or insufficient application can harm plant life and reduce productivity. The use of modern technology is a solution to this problem. Although automated techniques for sowing, weeding, crop harvesting, etc. have been proposed and implemented, none of the techniques are aimed to maintaining soil fertility. The study a
... Show MoreInvestigation of the adsorption of acid fuchsin dye (AFD) on Zeolite 5A is carried out using batch scale experiments according to statistical design. Adsorption isotherms, kinetics and thermodynamics were demonstrated. Results showed that the maximum removal efficiency was using zeolite at a temperature of 93.68751 mg/g. Experimental data was found to fit the Langmuir isotherm and pseudo second order kinetics with maximum removal of about 95%. Thermodynamic analysis showed an endothermic adsorption. Optimization was made for the most affecting operating variables and a model equation for the predicted efficiency was suggested.
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Optical burst switching (OBS) network is a new generation optical communication technology. In an OBS network, an edge node first sends a control packet, called burst header packet (BHP) which reserves the necessary resources for the upcoming data burst (DB). Once the reservation is complete, the DB starts travelling to its destination through the reserved path. A notable attack on OBS network is BHP flooding attack where an edge node sends BHPs to reserve resources, but never actually sends the associated DB. As a result the reserved resources are wasted and when this happen in sufficiently large scale, a denial of service (DoS) may take place. In this study, we propose a semi-supervised machine learning approach using k-means algorithm
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