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Our study aimed to investigate the effects of the polysaccharide-rich extract of Phragmites rhizoma (PEP) against water immersion restraint (WIR) stress and forced swimming-induced fatigue. Exposure to WIR stress significantly increased the ulcer index, bleeding score, the weight of the adrenal gland, blood glucose concentrations, total cholesterol, cortisol, and creatine kinase (CK). The weight of the spleen decreased significantly. In addition, myeloperoxidase (MPO) and thiobarbituric acid-reactive substance (TBARS) were significantly upregulated by WIR stress. The antioxidative factors such as glutathione (GSH) and superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) in the stomach were decreased by WIR stress. Alterations induced by WIR stress were effectively reversed by pretreatment with PEP. The swimming endurance capacity of mice was significantly prolonged by the oral administration of PEP. Swimming-induced fatigue significantly reduced the body weight; however, the injection of PEP inhibited the decrease of body weight. The PEP-treated group had significantly lower CK levels in plasma, an indicator of muscle damage. These results indicated that PEP has anti-stress and anti-fatigue effects, which are mediated by suppressing the hyperactivation of the hypothalamus-pituitary-adrenal axis, and antagonism of the oxidative damages induced by WIR stress and prolonged swimming times.

In this paper, we propose dissimilarity regularization with a multistage fusion stream for a synthetic aperture radar (SAR) and infrared (IR) sensor fusion using deep learning. The multistage fusion structures are composed of multiple layers for fusing all the feature maps generated by the convolutional neural networks. The proposed structure combines feature maps of equivalent levels, ensuring that the spatial information of the corresponding levels can be utilized for fusion. Dissimilarity regularization is the sum of the normalized cross-correlation between the features generated in two different single-sensor streams. The proposed regularization is added to the conventional learning problem of a single-sensor stream, and each single-sensor stream is promoted to learn the disparate types of features for fusion. To evaluate the proposed algorithm, we compare the recognition rate of the proposed algorithm with that of the conventional fusion approaches using the SAR and IR image databases. Finally, the effects of the proposed architecture and regularization on the fusion result are analyzed.

Recently, a proportionate-type diffusion LMS algorithm has been proposed by minimizing the mean-square deviation of an intermediate estimate in sparse distributed estimation problems. This algorithm enhances the convergence speed regardless of the sparseness of the vector of interest, but the gain calculation demands huge computational resources. We develop a computationally efficient version of this algorithm that maintains the advantage of the conventional algorithm. We also analyze the mean-square performance of the proposed algorithm without assuming Gaussian distribution of unknown vector estimates (the usual assumption in analysis of proportionate adaptive filters). HighlightsWe propose a computationally efficient proportionate-type diffusion LMS algorithm.Gains are proportional to the estimated mean square weight error.Mean-square performance analysis of the proposed algorithm is conducted without Gaussian assumption of unknown vector estimates.

Our study aimed to investigate the effects of the polysaccharide-rich extract of

We propose a new diffusion least mean squares algorithm that utilizes adaptive gains in the adaptation stage for the sparse distributed estimation problem. We derive the optimal gains that attain a minimum mean-square deviation and propose an adaptive gain control method. We provide the mean stability analysis to establish sufficient condition for the algorithm to converge in the mean sense. The algorithm achieves higher convergence speed than the sparsity-constrained algorithms, regardless of the sparsity of the vector of interest.

We propose a multistage fusion stream (MFS) and dissimilarity regularization (DisReg) for deep learning. The degree of similarity between the feature maps of a single-sensor stream is estimated using DisReg. DisReg is applied to the learning problems of each single-sensor stream, so they have distinct types of feature map. Each stage of the MFS fuses the feature maps extracted from single-sensor streams. The proposed scheme fuses information from heterogeneous sensors by learning new patterns that cannot be observed using only the feature map of a single-sensor stream. The proposed method is evaluated by testing its ability to automatically recognize targets in a synthetic aperture radar and infrared images. The superiority of the proposed fusion scheme is demonstrated by comparison with conventional algorithm.

We propose a decision-level architecture that combines synthetic aperture radar (SAR) and an infrared (IR) sensor for automatic target detection. We present a new size-based feature, called target-silhouette to reduce the number of false alarms produced by the conventional target-detection algorithm. Boolean Map Visual Theory is used to combine a pair of SAR and IR images to generate the target-enhanced map. Then basic belief assignment is used to transform this map into a belief map. The detection results of sensors are combined to build the target-silhouette map. We integrate the fusion mass and the target-silhouette map on the decision level to exclude false alarms. The proposed algorithm is evaluated using a SAR and IR synthetic database generated by SE-WORKBENCH simulator, and compared with conventional algorithms. The proposed fusion scheme achieves higher detection rate and lower false alarm rate than the conventional algorithms.

The previous study reported the biological effect of magnetic field exerted by acting on endocrine and anti-oxidant system. The present study aims to study whether ELF-MF (extremely low frequency magnetic field) affects the physiological endocrine systems such as thyroid and whether ELF-MF affects the defense system against oxidative stress when it alters the function of thyroid. Finally, we correlate the effects of MF on oxidative stress, and adrenal and thyroid with an environmental stress factor. We exposed sham or MF to rats for 5 or 25 days. After the exposure, we determined pain sensitivity, level of TSH, and free in plasma. We also assayed in whole brain, lipid peroxidation, the activity of enzymatic anti-oxidant defense including superoxide dismutase(SOD) and glutathione peroxidase (GPx), and non enzymatic defense such as reduced or oxidized glutathione contents. MF induced the hypersensitivity to thermal stimuli with the reduction of latency. and levels were also increased by the exposure of MF. In addition, we observed the rise of MDA level in rat brain by MF although the MF did not change superoxide dismutase and glutathione peroxidase activity. The effect of MF on both reduced and oxidized glutathione results in decrease in reduced or oxidized glutathione in whole brain. In every experiment, there was no significant difference in MF influence between short term (5 days) and long term (25 days) exposure. Taken together, MF exposure affects the thyroid hormonal control in brain. The elevated thyroid hormone acts on brain, leading to hyper-utilization of oxygen. This phenomenon may be correlated with oxidative stress resulting from MF exposure. In conclusion, we suggest that MF exposure may be an environmental stress by exerting oxidative stress.

We investigated the role of vitamin C or rutin on neuropathy and lung damage of diabetic mellitus (DM) rats. Norepinephrine content was significantly decreased in sciatic nerves'of DM rats compared with non-DM controls but vitamin C had no effect on decreases of norepinephrine. 2,4-dinitrophenylhydrazine (DNPH) incorporation, which is biomarker of protein oxidation, was increased in sciatic nerve of DM rats as compared with normal control. However, vitamin C had no effects on increases of DNPH incorporation. We measured the content of conjugated dienes (CD) as a biomarker of lipid oxidation in sciatic nerve. CD was increased in DM as compared with normal control. Vitamin C or rutin had no effects on increases of CD. However, Rutin plus vitamin C significantly decreased the content of CD as compared with DM rats. In lung of DM rats, DNPH incorporation or CD was increased as compared with normal control. Vitamin C or Rutin had no effects on increases of CD. However, Rutin plus vitamin C significantly decreased the content of DNPH incorporation or CD in lung tissue. Vitamin C caused marked pathological changes such as the increases of parenchyma and the thickening of alveolar septa in the lung of DM. Rutin had protective effects on the pathological changes in the lung of DM rats. In conclusion, Vitamin C had no effects on oxidative parameter, such as DNPH incorporation or CD, and on the decreases of norepinephrine content in DM rats. Vitamin C caused the marked pathological changes in the lung of DM rats but rutin had protective effects against the pathological changes.

This study evaluated the inhibitory action of luteolin-7-O-beta-D-glucuronopyranoside, luteolin which was isolated from Salix gilgiana leaves, and omeprazole on reflux esophagitis and gastritis in rats. Reflux esophagitis and gastritis were induced surgically and by the administration of indomethacin, respectively. The intraduodenal administration of luteolin-7-O-beta-D-glucuronopyranoside decreased the ulcer index, injury area, gastric volume and acid output, and increased the gastric pH compared with luteolin. Luteolin-7-O-beta-D-glucuronopyranoside significantly decreased the size of the gastric lesions that had been induced by exposing the gastric mucosa to indomethacin. The malondialdehyde content, which is the end product of lipid peroxidation, was increased significantly after inducing of reflux esophagitis. The malondialdehyde content was decreased by Luteolin-7-O-beta-D-glucuronopyranoside but not luteolin or omeprazole. Luteolin-7-O-beta-D-glucuronopyranoside has a more potent antioxidative effect than luteolin. Luteolin-7-O-beta-D-glucuronopyranoside is a promising drug for the treatment of reflux esophagitis and gastritis.