Wrocław University of Technology

About: Wrocław University of Technology is a education organization based out in Wrocław, Poland. It is known for research contribution in the topics: Laser & Fuzzy logic. The organization has 13115 authors who have published 31279 publications receiving 338694 citations.

Prediction of Chronic Kidney Disease - A Machine Learning Perspective

Abstract: Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis is required as soon as possible. Machine learning technique has become reliable for medical treatment. With the help of a machine learning classifier algorithms, the doctor can detect the disease on time. For this perspective, Chronic Kidney Disease prediction has been discussed in this article. Chronic Kidney Disease dataset has been taken from the UCI repository. Seven classifier algorithms have been applied in this research such as artificial neural network, C5.0, Chi-square Automatic interaction detector, logistic regression, linear support vector machine with penalty L1 & with penalty L2 and random tree. The important feature selection technique was also applied to the dataset. For each classifier, the results have been computed based on (i) full features, (ii) correlation-based feature selection, (iii) Wrapper method feature selection, (iv) Least absolute shrinkage and selection operator regression, (v) synthetic minority over-sampling technique with least absolute shrinkage and selection operator regression selected features, (vi) synthetic minority over-sampling technique with full features. From the results, it is marked that LSVM with penalty L2 is giving the highest accuracy of 98.86% in synthetic minority over-sampling technique with full features. Along with accuracy, precision, recall, F-measure, area under the curve and GINI coefficient have been computed and compared results of various algorithms have been shown in the graph. Least absolute shrinkage and selection operator regression selected features with synthetic minority over-sampling technique gave the best after synthetic minority over-sampling technique with full features. In the synthetic minority over-sampling technique with least absolute shrinkage and selection operator selected features, again linear support vector machine gave the highest accuracy of 98.46%. Along with machine learning models one deep neural network has been applied on the same dataset and it has been noted that deep neural network achieved the highest accuracy of 99.6%.

The effect of lipid oxidation on the water permeability of phospholipids bilayers

Abstract: The effect of lipid oxidation on water permeability of phosphatidylcholine membranes was investigated by means of both scattering stopped flow experiments and atomistic molecular dynamics simulations. Formation of water pores followed by a significant enhancement of water permeability was observed. The molecules of oxidized phospholipids facilitate pore formation and subsequently stabilize water in the membrane interior. A wide range of oxidation ratios, from 15 to 100 mol%, was considered. The degree of oxidation was found to strongly influence the time needed for the opening of a pore. In simulations, the oxidation ratio of 75 mol% was found to be a threshold for spontaneous pore formation in the tens of nanosecond timescale, whereas 15 mol% of oxidation led to significant water permeation in the timescale of seconds. Once a pore was formed, the water permeability was found to be virtually independent of the oxidation ratio.