The effects of the pretreatment of intravenous high dose methylprednisolone on Na(+)-K(+)/Mg(+2) ATPase and lipid peroxidation and early ultrastructural findings following middle cerebral artery occlusion in the rat

The sodium-potassium activated and magnesium dependent adenosine-5'-triphosphatase (Na(+)-K(+)/Mg(+2) ATPase EC.3.6.1.3.) activity and lipid peroxidation and early ultrastructural findings were determined in rat brain at the acute stage of ischaemia produced by permanent unilateral occlusion of the middle cerebral artery (MCA). The effects of the pretreatment with intravenous high-dose methylprednisolone (MP) on these biochemical indices and ultrastructural findings were also evaluated in the same model. The rats were divided into four groups. In group I, 10 rats were used to determine Na(+)-K(+)/Mg(+2) ATPase activity and the extent of lipid peroxidation by measuring the malondialdehyde (MDA) content and normal ultrastructural findings. In group II on 20 rats, only subtemporal craniectomy was done in order to determine the effects of the surgical procedure on these indices and findings. This group was treated intravenously with saline solution before occlusion. In group III with MCA occlusion, saline solution was administered intravenously to 20 rats in the same amount of methylprednisolone used in group IV, ten minutes before the occlusion. In Group IV, a single high-dose (30 mg/kg) of methylprednisolone was administered intravenously, ten minutes before occlusion in 20 rats. After occlusion of the middle cerebral artery, Na(+)-K(+)/Mg(+2) ATPase activity was decreased promptly in the first ten minutes in the ischaemic hemisphere and remained at a lower level than the contralateral hemispheres in the same group and the normal levels in group I, during 120 minutes of ischaemia. A single dose methylprednisolone pretreatment prohibited the inactivation of Na(+)-K(+)/Mg(+2) ATPase. On the other hand, there was significant difference in malondialdehyde content between group I and group III. Malondialdehyde levels were significantly increased following ischaemia and a non-significant increase was observed in the contralateral hemisphere. Methylprednisolone treatment significantly decreased malondialdehyde content on the side of the ischaemic hemisphere. We conclude that there is a positive relationship between membrane-bound enzyme Na(+)-K(+)/Mg(+2) ATPase activity, malondialdehyde content and early ultrastructural changes in the treated group with MP. These data suggest that the pretreatment injection of high doses (30 mg/kg) methylprednisolone contribute to the protection of the brain from ischaemia with stabilization of the cell membrane by effecting the lipid peroxidation and the activation of Na(+)-K(+)/Mg(+2) ATPase.     

Neural plasticity in adults with amblyopia

Amblyopia is a neuronal abnormality of vision that is often considered irreversible in adults. We found strong and significant improvement of Vernier acuity in human adults with naturally occurring amblyopia following practice. Learning was strongest at the trained orientation and did not transfer to an untrained task (detection), but it did transfer partially to the untrained eye (primarily at the trained orientation). We conclude that this perceptual learning reflects alterations in early neural processes that are localized beyond the site of convergence of the two eyes. Our results suggest a significant degree of plasticity in the visual system of adults with amblyopia.     

Real-Time Edge Follow: A Real-Time Path Search Approach

Real-time path search is the problem of searching a path from a starting point to a goal point in real-time. In dynamic and partially observable environments, agents need to observe the environment to track changes, explore to learn unknowns, and search suitable routes to reach the goal rapidly. These tasks frequently require real-time search. In this paper, we address the problem of real-time path search for grid-type environments; we propose an effective heuristic method, namely a real-time edge follow alternative reduction method (RTEF-ARM), which makes use of perceptual information in a real-time search. We developed several heuristics powered by the proposed method. Finally, we generated various grids (random-, maze-, and U-type), and compared our proposal with real-time A*, and its extended version real-time A* with n-look-ahead depth; we obtained very significant improvements in the solution quality.     

On the application of Harris hawks optimization (HHO) algorithm to the design of microchannel heat sinks

Engineering with Computers - A novel Harris hawks optimization algorithm is applied to microchannel heat sinks for the minimization of entropy generation. In the formulation of the heat transfer...     

Novel hybrid DNN approaches for speaker verification in emotional and stressful talking environments

Neural Computing and Applications - In this work, we conducted an empirical comparative study of the performance of text-independent speaker verification in emotional and stressful environments....     

A two-phase variable neighbourhood search algorithm for assembly line worker assignment and balancing problem type-II: an industrial case study

The assembly line worker assignment and balancing problem type-II (ALWABP-2) occurs when workers and tasks (where task times depend on workers’ skills) are to be simultaneously assigned to a fixed number of workstations with the goal of minimising the cycle time. In this study, a two-phase variable neighbourhood search (VNS) algorithm is proposed to solve the ALWABP-2 due to the NP-hard nature of this problem. In the first phase of the algorithm, a VNS approach is applied to assign tasks to workstations with the aim of minimising the cycle time while in the second phase, a variable neighbourhood descent method is applied to assign workers to workstations. The performance of the proposed algorithm is tested on well-known benchmark instances. In addition, the proposed algorithm has been used to solve a real case study from a consumer electronics company that manufactures LCD TVs. The results show that the algorithm is superior to the methods reported in the literature in terms of its higher efficiency and robustness. Furthermore, the algorithm is easy to implement and significantly improves the performance of the final assembly line for the investigated LCD TV real case study.     

Comparison of different classifier algorithms for diagnosing macular and optic nerve diseases

Abstract: The aim of this research was to compare classifier algorithms including the C4.5 decision tree classifier, the least squares support vector machine (LS-SVM) and the artificial immune recognition system (AIRS) for diagnosing macular and optic nerve diseases from pattern electroretinography signals. The pattern electroretinography signals were obtained by electrophysiological testing devices from 106 subjects who were optic nerve and macular disease subjects. In order to show the test performance of the classifier algorithms, the classification accuracy, receiver operating characteristic curves, sensitivity and specificity values, confusion matrix and 10-fold cross-validation have been used. The classification results obtained are 85.9%, 100% and 81.82% for the C4.5 decision tree classifier, the LS-SVM classifier and the AIRS classifier respectively using 10-fold cross-validation. It is shown that the LS-SVM classifier is a robust and effective classifier system for the determination of macular and optic nerve diseases.     

A new feature selection method on classification of medical datasets: Kernel F-score feature selection

In this paper, we have proposed a new feature selection method called kernel F-score feature selection (KFFS) used as pre-processing step in the classification of medical datasets. KFFS consists of two phases. In the first phase, input spaces (features) of medical datasets have been transformed to kernel space by means of Linear (Lin) or Radial Basis Function (RBF) kernel functions. By this way, the dimensions of medical datasets have increased to high dimension feature space. In the second phase, the F-score values of medical datasets with high dimensional feature space have been calculated using F-score formula. And then the mean value of calculated F-scores has been computed. If the F-score value of any feature in medical datasets is bigger than this mean value, that feature will be selected. Otherwise, that feature is removed from feature space. Thanks to KFFS method, the irrelevant or redundant features are removed from high dimensional input feature space. The cause of using kernel functions transforms from non-linearly separable medical dataset to a linearly separable feature space. In this study, we have used the heart disease dataset, SPECT (Single Photon Emission Computed Tomography) images dataset, and Escherichia coli Promoter Gene Sequence dataset taken from UCI (University California, Irvine) machine learning database to test the performance of KFFS method. As classification algorithms, Least Square Support Vector Machine (LS-SVM) and Levenberg–Marquardt Artificial Neural Network have been used. As shown in the obtained results, the proposed feature selection method called KFFS is produced very promising results compared to F-score feature selection.     

A novel data preprocessing method to estimate the air pollution (SO2): neighbor-based feature scaling (NBFS)

The forecasting of air pollution is important for living environment and public health. The prediction of SO₂ (sulfur dioxide), which is one of the indicators of air pollution, is a significant part of steps to be done in order to decrease the air pollution. In this study, a novel feature scaling method called neighbor-based feature scaling (NBFS) has been proposed and combined with artificial neural network (ANN) and adaptive network–based fuzzy inference system (ANFIS) prediction algorithms in order to predict the SO₂ concentration value is from air quality metrics belonging to Konya province in Turkey. This work consists of two stages. In the first stage, SO₂ concentration dataset has been scaled using neighbor-based feature scaling. In the second stage, ANN and ANFIS prediction algorithms have been used to forecast the SO₂ value of scaled SO₂ concentration dataset. SO₂ concentration dataset was obtained from Air Quality Statistics database of Turkish Statistical Institute. To constitute dataset, the mean values belonging to seasons of winter period have been used with the aim of watching the air pollution changes between dates of December, 1, 2003 and December, 30, 2005. In order to evaluate the performance of the proposed method, the performance measures including mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and IA (Index of Agreement) values have been used. After NBFS method applied to SO₂ concentration dataset, the obtained RMSE and IA values are 83.87–0.27 (IA) and 93–0.33 (IA) using ANN and ANFIS, respectively. Without NBFS, the obtained RMSE and IA values are 85.31–0.25 (IA) and 117.71–0.29 (IA) using ANN and ANFIS, respectively. The obtained results have demonstrated that the proposed feature scaling method has been obtained very promising results in the prediction of SO₂ concentration values.