Differential chromosome sensitivity to 5-azacytidine in Alzheimer''s disease

The chemiluminescence (CL) technique with scavengers for superoxide anion (superoxide dismutase) and hydrogen peroxide (catalase) was used to characterize the generation of reactive oxygen species (ROS) inside and outside the human neutrophil after stimulation with both soluble (formyl-methionyl-leucyl-phenylalanine, FMLP) and particulate (urate crystals, zymosan, oxidized LDL) stimuli. Depending on the stimulus used, ROS generation differed in composition and absolute amounts. The ratio between extracellularly and intracellularly produced ROS ranged from 0.3 (zymosan) to 4.2 (FMLP). While enhancing substantially FMLP-stimulated CL, horseradish peroxidase inhibited CL induced by particulate stimuli by 40-80%. Furthermore, an azide-insensitive and therefore peroxidase-independent part of CL was found in FMLP-, LDL- and zymosan-stimulated cells. The results indicate that different agonists may lead through distinct chemical pathways to neutrophil luminol-amplified light generation.     

MDMA ('Ecstasy') enhances 5-HT1A receptor density and 8-OH-DPAT-induced hypothermia: blockade by drugs preventing 5-hydroxytryptamine depletion

One week after a single administration of 3,4-methylenedioxymethamphetamine (MDMA HCI, 30 mg/kg i.p.), 5-HT1A receptor density was significantly increased by approximately 25-30% in the frontal cortex and hypothalamus of rats. The increased density correlated with the potentiation of the hypothermic response to the 5-HT1A receptor agonist 8-hydroxy-2-(di-n-propylamino)tetralin (8-OH-DPAT, 1 mg/kg s.c.). Hypothalamic 5-HT7 receptors, which also bind 8-OH-DPAT, were not changed, however, by MDMA. Fluoxetine (5 mg/kg s.c.), ketanserin (5 mg/kg s.c.) or haloperidol (2 mg/kg i.p.), given 15 min prior to MDMA, prevented the depletion of 5-hydroxytryptamine (5-HT) induced by MDMA and also blocked the effects of this neurotoxin on 5-HT1A receptor density and on 8-OH-DPAT-induced hypothermia. The protection afforded by drugs against 5-HT loss did not correlate, however, with the antagonism of the acute hyperthermic effect of MDMA. The present results indicate that drugs able to prevent or to attenuate MDMA-induced 5-HT loss also prevent the changes in 5-HT1A receptor density as well as the enhanced hypothermic response to the 5-HT1A receptor agonist 8-OH-DPAT in MDMA-treated rats.     

Cholesterol oxides accumulate in human cataracts

Glucocorticoids induce hyperinsulinemia, hyperglycemia, and depress glucose transport by aortic endothelium. High glucocorticoid doses are used for many diseases, but with unknown effects on brain glucose transport or metabolism. This study tested the hypothesis that glucocorticoids affect glucose transport or metabolism by brain microvascular endothelium. Male rats received dexamethasone (DEX) s.c. with sucrose feeding for up to seven days. Cerebral microvessels from rats treated with DEX/sucrose demonstrated increased GLUT1 and brain glucose extraction compared to controls. Glucose transport in vivo correlated with hyperinsulinemia. Pre-treatment with low doses of streptozotocin blunted hyperinsulinemia and prevented increased glucose extraction induced by DEX. In contrast, isolated brain microvessels exposed to DEX in vitro demonstrated suppression of 2-deoxyglucose uptake and glucose oxidation. We conclude that DEX/sucrose treatment in vivo increases blood-brain glucose transport in a manner that requires the effects of chronic hyperinsulinemia. These effects override any direct inhibitory effects of either hyperglycemia or DEX.     

Exporting water wave optimization concepts to modified simulated annealing algorithm for size optimization of truss structures with natural frequency constraints

This paper proposes an improved version of a recently proposed modified simulated annealing algorithm (MSAA) named as an improved MSAA (I-MSAA) to tackle the size optimization of truss structures with frequency constraint. This kind of problem is problematic because its feasible region is non-convex while the boundaries are highly non-linear. The main motivation is to improve the exploitative behavior of MSAA, taking concept from water wave optimization metaheuristic (WWO). An interesting concept of WWO is its breaking operation. Thirty functions extracted from the CEC2014 test suite and four benchmark truss optimization problems with frequency constraints are explored for the validity of the proposed algorithm. Numerical results indicate that I-MSAA is more reliable, stable and efficient than those found by other existing metaheuristics in the literature.

     

Multi-verse optimization algorithm- and salp swarm optimization algorithm-based optimization of multilevel inverters

Neural Computing and Applications - Renewable energy sources are installed into both distribution and transmission grids more and more with the introduction of smart grid concept. Hence, efficient...     

CFAR processing with switching exponential smoothers for nonhomogeneous environments

Conventional constant false alarm rate (CFAR) methods use a fixed number of cells to estimate the background variance. For homogeneous environments, it is desirable to increase the number of cells, at the cost of increased computation and memory requirements, in order to improve the estimation performance. For nonhomogeneous environments, it is desirable to use less number of cells in order to reduce the number of false alarms around the clutter edges. In this work, we present a solution with two exponential smoothers (first order IIR filters) having different time-constants to leverage the conflicting requirements of homogeneous and nonhomogeneous environments. The system is designed to use the filter having the large time-constant in homogeneous environments and to promptly switch to the filter having the small time constant once a clutter edge is encountered. The main advantages of proposed Switching IIR CFAR method are computational simplicity, small memory requirement (in comparison to windowing based methods) and its good performance in homogeneous environments (due to the large time-constant smoother) and rapid adaptation to clutter edges (due to the small time-constant smoother).     

Network regression with predictive clustering trees

Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation, where the value of a variable at a given node depends on the values of variables at the nodes it is connected with. This phenomenon is a direct violation of the assumption that data are independently and identically distributed. At the same time, it offers an unique opportunity to improve the performance of predictive models on network data, as inferences about one entity can be used to improve inferences about related entities. Regression inference in network data is a challenging task. While many approaches for network classification exist, there are very few approaches for network regression. In this paper, we propose a data mining algorithm, called NCLUS, that explicitly considers autocorrelation when building regression models from network data. The algorithm is based on the concept of predictive clustering trees (PCTs) that can be used for clustering, prediction and multi-target prediction, including multi-target regression and multi-target classification. We evaluate our approach on several real world problems of network regression, coming from the areas of social and spatial networks. Empirical results show that our algorithm performs better than PCTs learned by completely disregarding network information, as well as PCTs that are tailored for spatial data, but do not take autocorrelation into account, and a variety of other existing approaches.     

A note on double lacunary statistical $$\sigma$$-convergence of fuzzy numbers

Quite recently, Sava? (Appl Math Lett 21:134–141, 2008), defined the lacunary statistical analogue for double sequence \(X=\{X_{k,l}\}\) of fuzzy numbers as follows: a double sequence \(X=\{X_{k,l}\}\) is said to be lacunary P-statistically convergent to \(X_{0}\) provided that for each \(\epsilon >0\)

$ P-\lim_{r,s}\frac{1}{h_{r,s}}\left | \{(k,l)\in I_{r,s}: d(X_{k,l },X_0)\geq \epsilon\}\right|= 0. $

In this paper we introduce and study double lacunary \(\sigma\)-statistical convergence for sequence of fuzzy numbers and also we get some inclusion theorems.

     

A new ECG beat clustering method based on kernelized fuzzy c-means and hybrid ant colony optimization for continuous domains

The kernelized fuzzy c-means algorithm uses kernel methods to improve the clustering performance of the well known fuzzy c-means algorithm by mapping a given dataset into a higher dimensional space non-linearly. Thus, the newly obtained dataset is more likely to be linearly seprable. However, to further improve the clustering performance, an optimization method is required to overcome the drawbacks of the traditional algorithms such as, sensitivity to initialization, trapping into local minima and lack of prior knowledge for optimum paramaters of the kernel functions. In this paper, to overcome these drawbacks, a new clustering method based on kernelized fuzzy c-means algorithm and a recently proposed ant based optimization algorithm, hybrid ant colony optimization for continuous domains, is proposed. The proposed method is applied to a dataset which is obtained from MIT–BIH arrhythmia database. The dataset consists of six types of ECG beats including, Normal Beat (N), Premature Ventricular Contraction (PVC), Fusion of Ventricular and Normal Beat (F), Artrial Premature Beat (A), Right Bundle Branch Block Beat (R) and Fusion of Paced and Normal Beat (f). Four time domain features are extracted for each beat type and training and test sets are formed. After several experiments it is observed that the proposed method outperforms the traditional fuzzy c-means and kernelized fuzzy c-means algorithms.