A two-phase decision support framework for the automatic screening of digital fundus images

In this paper we give a brief review on the present status of automated detection systems describe for the screening of diabetic retinopathy. We further detail an enhanced detection procedure that consists of two steps. First, a pre-screening algorithm is considered to classify the input digital fundus images based on the severity of abnormalities. If an image is found to be seriously abnormal, it will not be analysed further with robust lesion detector algorithms. As a further improvement, we introduce a novel feature extraction approach based on clinical observations. The second step of the proposed method detects regions of interest with possible lesions on the images that previously passed the pre-screening step. These regions will serve as input to the specific lesion detectors for detailed analysis. This procedure can increase the computational performance of a screening system. Experimental results show that both two steps of the proposed approach are capable to efficiently exclude a large amount of data from further processing, thus, to decrease the computational burden of the automatic screening system.     

Nonlinear model predictive control with logic constraints for COVID-19 management

Nonlinear Dynamics - The management of COVID-19 appears to be a long-term challenge, even in countries that have managed to suppress the epidemic after their initial outbreak. In this paper, we...     

Demonstration of Allium sativum Extract Inhibitory Effect on Biodeteriogenic Microbial Strain Growth,Biofilm Development,and Enzymatic and Organic Acid Production

To the best of our knowledge, this is the first study demonstrating the efficiency of Allium sativum hydro-alcoholic extract (ASE) againstFigure growth, biofilm development, and soluble factor production of more than 200 biodeteriogenic microbial strains isolated from cultural heritage objects and buildings. The plant extract composition and antioxidant activities were determined spectrophotometrically and by HPLC–MS. The bioevaluation consisted of the qualitative (adapted diffusion method) and the quantitative evaluation of the inhibitory effect on planktonic growth (microdilution method), biofilm formation (violet crystal microtiter method), and production of microbial enzymes and organic acids. The garlic extract efficiency was correlated with microbial strain taxonomy and isolation source (the fungal strains isolated from paintings and paper and bacteria from wood, paper, and textiles were the most susceptible). The garlic extract contained thiosulfinate (307.66 ± 0.043 µM/g), flavonoids (64.33 ± 7.69 µg QE/g), and polyphenols (0.95 ± 0.011 mg GAE/g) as major compounds and demonstrated the highest efficiency against the Aspergillus versicolor (MIC 3.12–6.25 mg/mL), A. ochraceus (MIC: 3.12 mg/mL), Penicillium expansum (MIC 6.25–12.5 mg/mL), and A. niger (MIC 3.12–50 mg/mL) strains. The extract inhibited the adherence capacity (IIBG% 95.08–44.62%) and the production of cellulase, organic acids, and esterase. This eco-friendly solution shows promising potential for the conservation and safeguarding of tangible cultural heritage, successfully combating the biodeteriogenic microorganisms without undesirable side effects for the natural ecosystems.