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The Effect of Oral Ascorbic Acid Pretreatment on Feeding Changes Following Injection in Nucleus Accumbens Shell in Adult Male Rats
Salari S,Abbasnejad M,Badreh F,Esmaeili Mahani S
Tehran University Medical Journal , 2012,
Abstract: Background: Ascorbic acid (AA) is not synthesized in the brain but it is actively transported through blood-brain barrier by SVCT2 cotransporter and it is stored in high concentrations with heterogeneous distribution in areas such as nucleus accumbens shell (AcbSh) in the mammalian brain. Previous studies have shown that Ascorbic acid injection into AcbSh decreases feeding; therefore, in the present study we evaluated the effects of oral Ascorbic acid pretreatment on changes in feeding upon its injection in AcbSh in adult male rats.Methods: Sixty-three adult male rats (220-280 g) were divided into five treatment and five pretreatment groups. The treatment groups included the control (intact) group, sham-operated Ascorbic acid group that received normal saline as vehicle, and three other groups that received different doses of ascorbic acid (10, 50 and 250 μg/rat) by injection into AcbSh for four days. The pretreatment groups received Ascorbic acid (100 mg/kg) for 15 days via gastric gavage before receiving the aforementioned doses in treatment groups into intra nucleus AcbSh. Feeding measurement was repeated every 12 hours by automatic metabolic cage.Results: The results indicated that all injected doses of Ascorbic acid (10, 50 and 250 μg/rat) into nucleus accumbens shell decrease food intake (P<0.05) in rats and oral Ascorbic acid pretreatment had no effects in this regard.Conclusion: Our findings show that ascorbic acid is an effective factor in feeding regulation. Oral pretreatment seems to have no influence on the central effects of ascorbic acid in the nucleus accumbens shell.
Olive (Olea europaea L.) leaf extract prevents motor deficit in streptozotocin-induced diabetic rats
Saeed Esmaeili Mahani,Ayat Kaeidi
Physiology and Pharmacology , 2012,
Abstract: Introduction:Olive leaves have been recommended in the scientific literature and traditional medicine as a cure for the treatment of diabetes and this plant has powerful antioxidants and neuroprotective effects. Here, we studied the possible effects of olive leaf extract (OLE) on motor deficits in diabetic neuropathy. Methods:The rotarod treadmill test was used to access motor coordination in streptozotocin-induced diabetic rats. Different doses of OLE (100, 300 and 500 mg/kg, i.g.) were given. Serum glucose and insulin levels were assessed by specific kits. Results:Four weeks after diabetes induction, glucose level was significantly decreased and insulin concentration increased (P<0.001). The rotarod treadmill test showed a marked impairment of the motor coordination of the diabetic animals (P<0.001). The retention time of the diabetic animals was reduced by 61.2% compared to the control animals, whereas treatment with 300 mg/kg OLE increased retention time to 83.6% of the control values. That dose had a moderate lowering effect on serum glucose with no effect on insulin levels. Conclusion:The results suggest that olive leaf extract has protective effects against high glucose-induced motor defects in diabetic rats.
Olive (Olea europaea L.) leaf extract and its main component (oleuropein) mitigate the development of morphine physical dependence in rats
Saeed Esmaeili Mahani,Leila Zare
Physiology and Pharmacology , 2013,
Abstract: Introduction: Recently, it has been demonstrated that olive leaf extract and its main component have calcium channel blocker, anti-inflammatory and anti-oxidative properties. However, the effects of olive leaf extract on opioid dependence have not yet been clarified. Methods: To develop morphine dependence, morphine was injected twice daily for 7 days according to an escalating dose in rats. On day 7, the animals received naloxone (3 mg/kg, i.p.) 5 h after the last injection of morphine. Withdrawal signs (weight loss, abdominal contraction, diarrhea, teeth chattering, jumping, grooming and ptosis) were evaluated during 1h after naloxone. To determine the effect of OLE and oleuropein on the development of morphine dependence, OLE was given at doses of 200, 300 and 500 mg/kg and oleuropin with 10 mg/kg (i.p.) concomitant with morphine. Results: Our results showed that rats chronically injected with morphine showed physical dependence. OLE (300 mg/kg) and oleuropin (10 mg/kg) could attenuate naloxone-induced withdrawal syndrome. Conclusion: Our data revealed that olive leaf extract had a beneficial effect on chronic morphine-induced side effects such as physical dependence and can be useful in the period of drug withdrawal and its main component, oleuropein, is responsible for such observed effects.
The Effect of Intrahippocampal Injection of Ascorbic Asid on Spatial Learning and Memory in Adult Male Rats
khadije esmaeilpour,mehdi abbasnejad,saeed esmaeili mahani,yaser masomi ardakani
Physiology and Pharmacology , 2010,
Abstract: Introduction: Ascorbic acid (AA) is present in high concentrations with heterogeneous distribution in the mammalian brain. Previous studies have shown that release of various neurotransmitters such as glutamate, acetylcholine and dopamine might be involved in the central AA release. On the other hand all of these neurotransmitters and the region CA1 of the hippocampus are involved in learning and memory. The aim of the present study was to evaluate the effects of ascorbic acid injection in the CA1 region on spatial learning and memory in adult male rats. Methods: 42 adult male NMRI rats (250-300 g) divided into 6 groups were used in this study. They included control group that received no injection, sham-operated group that received normal saline injection as vehicle and four groups that received different doses of ascorbic acid (6, 12, 24 and 48 μg/rat). All injections were given in 5 consecutive days and 30 min after each injection, the rats were tested in the Morris Water Maze test to measure learning and memory task. Spatial learning and memory parameters were subjected to analysis of variance (ANOVA). Results: The results indicated that intrahippocampal microinjection of AA (12 and 24 μg/rat) significantly increased some spatial learning and memory parameters such as escape latency and path length to reach the hidden platform. Conclusion: Our findings show that AA injection into the CA1 region has a negative effect on spatial learning and memory.
Evaluation of the effect of the ergonomic principles’ instructions on the dental students’ postures; an ergonomic assessment
Yaghobee S,Esmaeili V
Journal of Dental Medicine , 2010,
Abstract:
Modeling Ventricular Muscle Cell by the Least Parameters
S. Esmaeili,S. H. Sabzposhan
Asian Journal of Biomedical and Pharmaceutical Sciences , 2012,
Abstract: Modeling the dynamics of ventricular muscle cell require models thatreproduce realistic characteristics in cell. In this paper, we present a minimalmodel for ventricular muscle cell that is designed to reproduce importantcharacteristics of cardiac cell, including action potential (AP) amplitudes andmorphologies, excitability and all or none criteria. To generate the minimalmodel, first, we presented a general form of a nonlinear model with twostatevariables for the cell, and then parameters of the model have beenfitted by using the theories and experiments that has done on COR software.Findings show the proposed minimal model has important features forexample; it is simple and is computationally efficient.
Bias correction of satellite rainfall estimation using a radar-gauge product
K. Tesfagiorgis,S. E. Mahani,R. Khanbilvardi
Hydrology and Earth System Sciences Discussions , 2010, DOI: 10.5194/hessd-7-8913-2010
Abstract: Satellite rainfall estimates can be used in operational hydrologic prediction, but are prone to systematic errors. The goal of this study is to seamlessly blend a radar-gauge product with a corrected satellite product that fills gaps in radar coverage. To blend different rainfall products, they should have similar bias features. The paper presents a pixel by pixel method, which aims to correct biases in hourly satellite rainfall products using a radar-gauge rainfall product. Bias factors are calculated for corresponding rainy pixels, and a desired number of them are randomly selected for the analysis. Bias fields are generated using the selected bias factors. The method takes into account spatial variation and random errors in biases. Bias field parameters were determined on a daily basis using the Shuffled Complex Evolution optimization algorithm. To include more sources of errors, ensembles of bias factors were generated and applied before bias field generation. The procedure of the method was demonstrated using a satellite and a radar-gauge rainfall data for several rainy events in 2006 for the Oklahoma region. The method was compared with bias corrections using interpolation without ensembles, the ratio of mean and maximum ratio. Results show the method outperformed the other techniques such as mean ratio, maximum ratio and bias field generation by interpolation.
Efficient SIMD RNG for Varying-Parameter Streams: C++ Class BatchRNG
Alireza S. Mahani,Mansour T. A. Sharabiani
Computer Science , 2014,
Abstract: Single-Instruction, Multiple-Data (SIMD) random number generators (RNGs) take advantage of vector units to offer significant performance gain over non-vectorized libraries, but they often rely on batch production of deviates from distributions with fixed parameters. In many statistical applications such as Gibbs sampling, parameters of sampled distributions change from one iteration to the next, requiring that random deviates be generated one-at-a-time. This situation can render vectorized RNGs inefficient, and even inferior to their scalar counterparts. The C++ class BatchRNG uses buffers of base distributions such uniform, Gaussian and exponential to take advantage of vector units while allowing for sequences of deviates to be generated with varying parameters. These small buffers are consumed and replenished as needed during a program execution. Performance tests using Intel Vector Statistical Library (VSL) on various probability distributions illustrates the effectiveness of the proposed batching strategy.
SIMD Parallel MCMC Sampling with Applications for Big-Data Bayesian Analytics
Alireza S. Mahani,Mansour T. A. Sharabiani
Computer Science , 2013, DOI: 10.1016/j.csda.2015.02.010
Abstract: Computational intensity and sequential nature of estimation techniques for Bayesian methods in statistics and machine learning, combined with their increasing applications for big data analytics, necessitate both the identification of potential opportunities to parallelize techniques such as MCMC sampling, and the development of general strategies for mapping such parallel algorithms to modern CPUs in order to elicit the performance up the compute-based and/or memory-based hardware limits. Two opportunities for Single-Instruction Multiple-Data (SIMD) parallelization of MCMC sampling for probabilistic graphical models are presented. In exchangeable models with many observations such as Bayesian Generalized Linear Models, child-node contributions to the conditional posterior of each node can be calculated concurrently. In undirected graphs with discrete nodes, concurrent sampling of conditionally-independent nodes can be transformed into a SIMD form. High-performance libraries with multi-threading and vectorization capabilities can be readily applied to such SIMD opportunities to gain decent speedup, while a series of high-level source-code and runtime modifications provide further performance boost by reducing parallelization overhead and increasing data locality for NUMA architectures. For big-data Bayesian GLM graphs, the end-result is a routine for evaluating the conditional posterior and its gradient vector that is 5 times faster than a naive implementation using (built-in) multi-threaded Intel MKL BLAS, and reaches within the striking distance of the memory-bandwidth-induced hardware limit. The proposed optimization strategies improve the scaling of performance with number of cores and width of vector units (applicable to many-core SIMD processors such as Intel Xeon Phi and GPUs), resulting in cost-effectiveness, energy efficiency, and higher speed on multi-core x86 processors.
Fast Estimation of Multinomial Logit Models: R Package mnlogit
Asad Hasan,Wang Zhiyu,Alireza S. Mahani
Statistics , 2014,
Abstract: We present R package mnlogit for training multinomial logistic regression models, particularly those involving a large number of classes and features. Compared to existing software, mnlogit offers speedups of 10x-50x for modestly sized problems and more than 100x for larger problems. Running mnlogit in parallel mode on a multicore machine gives an additional 2x-4x speedup on up to 8 processor cores. Computational efficiency is achieved by drastically speeding up calculation of the log-likelihood function's Hessian matrix by exploiting structure in matrices that arise in intermediate calculations.
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