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Liver disease has been known for a long time to affect brain function. We now report the function of opioidergic and dopaminergic antagonists on both spatial and object novelty detection deficits induced by hepatic encephalopathy (HE) following bile duct ligation (BDL), a model of chronic liver disease. Assessment of spatial and object novelty detection memories was carried out in the non-associative task. It consists of placing mice in an open field containing five objects and, after three sessions of habituation, examining their reactivity to object displacement (spatial novelty) and object substitution (object novelty). Both spatial and object novelty detection memories were impaired by BDL after 4 weeks. In the BDL mice, pre-test intraperitoneal administration of naloxone (μ-opioidergic receptor antagonist) at dose of 0.9mg/kg restored while sulpiride (D2-like dopamine receptor antagonist) at dose of 40mg/kg potentiated object novelty detection memory deficit. However, SCH23390 (D1-like dopamine receptor antagonist) at dose of 0.04mg/kg or sulpiride (20mg/kg) restored spatial novelty detection memory deficit. Moreover, SCH23390 or sulpiride impaired while naloxone did not alter both memories in sham-operated mice. Furthermore, subthreshold dose co-administration of dopaminergic antagonists together or each one plus naloxone did not alter both memory impairments in BDL mice, while all of three co-administration groups impaired object novelty detection and co-administration of naloxone plus sulpiride impaired spatial detection memory in sham-operated mice. In conclusion, we suggest that opioidergic and dopaminergic systems through separate pathways may contribute in memory impairments induced by BDL in the non-associative task.

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In recent years, wireless sensor networks have been studied in numerous cases. One of the important problems studied in these networks is the optimal deployment of sensors to obtain the maximum of coverage. Hence, in most studies, optimization algorithms have been used to achieve the maximum coverage. Optimization algorithms are divided into two groups of local and global optimization algorithms. Global algorithms generally use a random method based on an evolutionary process. In most of the conducted research, the environment model and, sometimes, the layout of sensors in the network have been considered in a very simplified form. In this research, by raster and vector modeling of the environment in two-and three-dimensional spaces, the function of global optimization algorithms was compared and assessed for optimal deployment of sensors and a vector environment model was used as a more accurate model. Since the purpose of this paper is to compare the performance and results of global algorithms, the studied region and the implementation conditions considered are the same for all applied algorithms. In this article, some optimization methods are considered for sensor deployment including genetic algorithms, L-BFGS, VFCPSO and CMA-ES, and the implementation and assessment criteria of algorithms for deployment of wireless sensor network are considered some factors such as the optimal coverage amount, their coverage accuracy towards the environment model and convergence speed of the algorithms. On the other hand, in this paper, the probability coverage model is implemented for each of the global optimization algorithms. The results of these implementations show that the presence of more complex parameters in environment model and coverage produce accurate results that are more consistent with reality. Nonetheless, it may reduce the time efficiency of algorithms.