Pharmacology and subcellular distribution of [3H]rilmenidine binding sites in rat brain

We have previously reported that in rat brain membranes, [3H]rilmenidine, in addition to labelling alpha2-adrenoceptors and the I2B-subtype of imidazoline receptor binding site (I2B-RBS), may label an additional I-RBS population, distinct from previously classified I1-RBS and I2-RBS. In this study, using crude or fractionated rat brain membranes we examined the possible association of [3H]rilmenidine-labelled I-RBS with the A- and B-isoforms of monoamine oxidase (MAO) by studying the inhibition of [3H]rilmenidine binding by a number of MAO inhibitors; and comparing the maximal binding density (Bmax) and subcellular distribution of [3H]rilmenidine binding sites with that of MAO-A and MAO-B catalytic sites labelled by [3H]RO41-1049 and [3H]RO19-6327 and 12-RBS labelled by [3H]2-BFI. Inhibition of [3H]rilmenidine binding by all MAO inhibitors tested produced very shallow curves (slope 0.29-0.56). Clorgyline and moclobemide (selective MAO-A inhibitors) displayed moderate affinities (60-140 nM), while pargyline (non-selective MAO-inhibitor), RO41-1049 (selective MAO-A inhibitor) and RO19-6327 (selective MAO-B inhibitor) exhibited very low affinities (> 2 microM) for 50-75% of [3H]rilmenidine-labelled I-RBS in crude brain membranes and even lower affinity for the remaining binding. Under identical buffer conditions, the Bmax of [3H]rilmenidine-labelled I-RBS (1.45+/-0.14 pmol/mg protein) was considerably lower than those of MAO-A (13.10+/-0.15 pmol/mg) and MAO-B (10.35+/-0.50 pmol/mg) sites. These results suggest that [3H]rilmenidine does not interact directly with the active catalytic site of either MAO enzyme and could at best only associate with a subpopulation of MAO molecules. Binding studies on five fractions of rat cortex homogenates-nuclear (N), heavy (M) and light (L) mitochondrial, microsomal non-mitochondrial (P), and soluble cytosolic (S) fractions-revealed that 45% of total [3H]rilmenidine binding was present in the P fraction cf. 20 and 23% in the M and L fractions, in contrast to [3H]RO19-6327 and [3H]2-BFI which bound 11-13% in the P fraction and 36-38% and 35-44% in the M and L fractions, respectively. Binding of all ligands in the N fraction was 6-15% of total. These studies reveal that [3H]rilmenidine-labelled I-RBS, unlike the I2-RBS, are not predominantly associated with mitochondrial fractions containing the MAO enzymes (and cytochrome oxidase activity), but appear to be distributed in both the mitochondrial and plasma membrane fractions in rat cerebral cortex.     

Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design

The present work aimed to evaluate and optimize the design of an artificial neural network (ANN) combined with an optimization algorithm of genetic algorithm (GA) for the calculation of slope stability safety factors (SF) in a pure cohesive slope. To make datasets of training and testing for the developed predictive models, 630 finite element limit equilibrium (FELE) analyses were performed. Similar to many artificial intelligence-based solutions, the database was involved in 189 testing datasets (e.g., 30% of the entire database) and 441 training datasets; for example, a range of 70% of the total database. Moreover, variables of multilayer perceptron (MLP) algorithm (for example, number of nodes in any hidden layer) and the algorithm of GA like population size was optimized by utilizing a series of trial and error process. The parameters in input, which were used in the analysis, consist of slope angle (β), setback distance ratio (b/B), applied stresses on the slope (F_y) and undrained shear strength of the cohesive soil (C_u) where the output was taken SF. The obtained network outputs for both datasets from MLP and GA-MLP models are evaluated according to many statistical indices. A total of 72 MLP trial and error (e.g., parameter study) the optimal architecture of 4 × 8 × 1 were determined for the MLP structure. Both proposed techniques result in a proper performance; however, according to the statistical indices, the GA–MLP model can somewhat accomplish the least mean square error (MSE) when compared to MLP. In an optimized GA–MLP network, coefficient of determination (R²) and root mean square error (RMSE) values of (0.975, and 0.097) and (0.969, and 0.107) were found, respectively, to both of the normalized training and testing datasets.

     

Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network

Engineering with Computers - The advent of new data-mining techniques and, more recently, swarm-based optimization algorithms have antiquated traditional models in the field of energy performance...     

Ignite-GPU: a GPU-enabled in-memory computing architecture on clusters

The Journal of Supercomputing - During recent years, big data explosion and the increase in main memory capacity, on the one hand, and the need for faster data processing, on the other hand, have...     

A firefly algorithm for power management in wireless sensor networks (WSNs)

The Journal of Supercomputing - In wireless sensor networks (WSNs), designing a stable, low-power routing protocol is a major challenge because successive changes in links or breakdowns destabilize...     

Multiobjective invasive weed optimization: Application to analysis of Pareto improvement models in electricity markets

This paper presents a proposal for multiobjective Invasive Weed Optimization (IWO) based on nondominated sorting of the solutions. IWO is an ecologically inspired stochastic optimization algorithm which has shown successful results for global optimization. In the present work, performance of the proposed nondominated sorting IWO (NSIWO) algorithm is evaluated through a number of well-known benchmarks for multiobjective optimization. The simulation results of the test problems show that this algorithm is comparable with other multiobjective evolutionary algorithms and is also capable of finding better spread of solutions in some cases. Next, the proposed algorithm is employed to study the Pareto improvement model in two complex electricity markets. First, the Pareto improvement solution set is obtained for a three-player oligopolistic electricity market with a nonlinear demand function. Then, the IEEE 30-bus power system with transmission constraints is considered, and the Pareto improvement solutions are found for the model with deterministic cost functions. In addition, NSIWO algorithm is used to analyze this system with stochastic cost data in a risk management problem which maximizes the expected total profit but minimizes the profit risk in the market.     

Pd/Cu-Free Cobalt-Catalyzed Suzuki and Heck Using Green Bio-Magnetic Hybrid and DFT-Based Theoretical Study

Catalysis Letters - Several highly efficient and magnetically recyclable cobalt catalytic systems were prepared using magnetic chitosan and some safe and available organic compounds...     

Analytical Modeling of Short-Channel Fully-Depleted Triple Work Function Metal Gate (TWFMG) SOI MESFET

Silicon - In this paper, a new structure: triple work function metal gate SOI MESFET, intended for integration into the deep-submicron CMOS technology, is proposed. The gate of the device consists...     

Application of neuro-fuzzy techniques in oil pipeline ultrasonic nondestructive testing

This paper presents a novel approach to the problem of nondestructive pipeline testing using ultrasonic imaging. The identification of the flaw type and its dimensions are the most important problems in the pipeline inspection. Unlike typical methods, a decision based neural network is used for the detection of flaws. We train a generalized regression neural network to determine the dimensions of the corrosions and generate the whole image of both the internal and external walls of the oil pipeline. As an improvement to the detection algorithm, we introduce fuzzy decision-based neural network algorithms for the detection and classification of the corrosions. The simulation and experimental systems results show that these new methods outperform the existing methods.