Baicalein administration protects against pentylenetetrazole-induced chronic epilepsy in rats

Purpose: To investigate the protective effect of baicalein against chronic seizures in pentylenetetrazole induced epilepsy in a rat model.Methods: A rat model of chronic epilepsy was prepared by administration of pentylenetetrazole at a dose of 35 mg/kg to Sprague-Dawley rats. The animals were divided into 6 groups (5 rats/group): normal control, model (untreated epilepsy) and four treatment groups that received separately, intraperitoneal injection of 20, 30, 40 and 50 mg/kg baicalein, respectively, on alternate days for 30 days. On each day following baicalein treatment, behavioural alterations in the  rats were assessed.Results: Analyses of behavioural changes revealed significant (p < 0.05) decrease in pentylenetetrazole-induced convulsions by baicalein treatment at a dose of 50 mg/kg. Immunohistochemical studies revealed that treatment with baicalein caused significant (p < 0.05) dosedependent reductions in the levels of inducible nitric oxide synthase (iNOS). Baicalein treatment inhibited alterations in cell morphology, and also inhibited pentylenetetrazole-induced increase in the proportion of glial fibrillary acidic protein (GFAP)-positive cells in a dose-dependent manner (p < 0.05). Real-time polymerase chain reaction (RT-PCR) analysis showed that baicalein significantly inhibited the expression of mRNA of NR1 subunit N methyl D aspartic acid (NMDA) receptor, without any effect on the expression of the NR2b (N-methyl D-aspartate receptor subtype 2B ) subunit mRNA (p < 0.05).Conclusion: These results indicate that baicalein inhibits pentylenetetrazole-induced chronic seizures in rats via reduction in astrocytes, inhibition of neuronal death and reduction of NR1 mRNA expression. Thus, baicalein has a potential for development into a new drug for the treatment of chronic epilepsy.Keywords: Pentylenetetrazole, Epilepsy, Baicalein, Convulsion, Inhibition, behavioural changes, Hippocampu

Biological Invasion and Coexistence in Intraguild Predation

Invasion of an exotic species initiated by its local introduction is considered subject to intraguild predation (IGP). Mathematically, the system dynamics is described by three nonlinear diffusion-reaction equations in two spatial dimensions. The key factors that determine successful invasion are investigated by means of extensive numerical simulations. The results reveal high asymmetry. An exotic species can invade successfully if it acted as the top predator and engaged in IGP, and the IGP interactions of the postinvasion web will be kept. While the exotic species were introduced as the intraguild prey (IGprey), they invade and spread through patchy invasion which corresponds to the invasion at the edge of extinction. Increase of the IGprey's dispersal rate and decrease of the IGpredator's may make the IGprey invade. But the interactions of the postinvasion web will change from IGP to competition, which is absolutely different from the first case. Finally, the common existence of IGP was explored once again from the perspective of biological invasion

Dual-Stage Approach Toward Hyperspectral Image Super-Resolution

Hyperspectral image produces high spectral resolution at the sacrifice of spatial resolution. Without reducing the spectral resolution, improving the resolution in the spatial domain is a very challenging problem. Motivated by the discovery that hyperspectral image exhibits high similarity between adjacent bands in a large spectral range, in this paper, we explore a new structure for hyperspectral image super-resolution (DualSR), leading to a dual-stage design, i.e., coarse stage and fine stage. In coarse stage, five bands with high similarity in a certain spectral range are divided into three groups, and the current band is guided to study the potential knowledge. Under the action of alternative spectral fusion mechanism, the coarse SR image is super-resolved in band-by-band. In order to build model from a global perspective, an enhanced back-projection method via spectral angle constraint is developed in fine stage to learn the content of spatial-spectral consistency, dramatically improving the performance gain. Extensive experiments demonstrate the effectiveness of the proposed coarse stage and fine stage. Besides, our network produces state-of-the-art results against existing works in terms of spatial reconstruction and spectral fidelity

Monitoring Land Surface Deformation with Satellite ScanSAR Images: Case Studies on Large Earthquakes in China

This chapter presents a new application of scanning interferometric synthetic aperture radar (ScanSAR) interferometry in monitoring land surface deformation caused by large earthquakes. To make better use of the ScanSAR data and obtain a wider deformation observation, this research studied and analyzed certain key elements of ScanSAR interferometry, including coherence, co-registering, methods of removing orbit errors, correction of atmosphere effects, and geoid undulation. The wide swath mode (WSM) is also known as the ScanSAR mode by which synthetic aperture time is shared by adjacent sub-swaths and azimuth resolution that is traded off for a wider coverage. So, it is possible to monitor a larger area of earthquake deformation. In this study, we obtained ScanSAR and Image Mode (IM) data and analyzed coherence, co-registering, methods of removing orbit errors, correction of atmosphere effects, and geoid undulation to monitor land surface deformation caused by large earthquakes in the 405 × 405 km field of the Wenchuan earthquake and Yutian earthquake, respectively, in China. The results obtained agree well with that of the investigations of the crustal motion in the study areas

An Efficient Universal Bee Colony Optimization Algorithm

The artificial bee colony algorithm is a global optimization algorithm. The artificial bee colony optimization algorithm is easy to fall into local optimal. We proposed an efficient universal bee colony optimization algorithm (EUBCOA). The algorithm adds the search factor u and the selection strategy of the onlooker bees based on local optimal solution. In order to realize the controllability of algorithm search ability, the search factor u is introduced to improve the global search range and local search range. In the early stage of the iteration, the search scope is expanded and the convergence rate is increased. In the latter part of the iteration, the algorithm uses the selection strategy to improve the algorithm accuracy and convergence rate. We select ten benchmark functions to testify the performance of the algorithm. Experimental results show that the EUBCOA algorithm effectively improves the convergence speed and convergence accuracy of the ABC algorithm

Concise and Effective Network for 3D Human Modeling from Orthogonal Silhouettes

In this paper, we revisit the problem of 3D human modeling from two orthogonal silhouettes of individuals (i.e., front and side views). Different from our prior work {\cite{wang2003virtual}}, a supervised learning approach based on \textit{convolutional neural network} (CNN) is investigated to solve the problem by establishing a mapping function that can effectively extract features from two silhouettes and fuse them into coefficients in the shape space of human bodies. A new CNN structure is proposed in our work to exact not only the discriminative features of front and side views and also their mixed features for the mapping function. 3D human models with high accuracy are synthesized from coefficients generated by the mapping function. Existing CNN approaches for 3D human modeling usually learn a large number of parameters (from {8.5M} to {355.4M}) from two binary images. Differently, we investigate a new network architecture and conduct the samples on silhouettes as input. As a consequence, more accurate models can be generated by our network with only {2.4M} coefficients. The training of our network is conducted on samples obtained by augmenting a publicly accessible dataset. Learning transfer by using datasets with a smaller number of scanned models is applied to our network to enable the function of generating results with gender-oriented (or geographical) patterns

A dynamic neighborhood learning-based gravitational search algorithm

Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named Kbest, which stores those superior agents after fitness sorting in each iteration. Since the global property of Kbest remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence. To address these problems, in this paper, we propose a dynamic neighborhood learning (DNL) strategy to replace the Kbest model and thereby present a DNL-based GSA (DNLGSA). The method incorporates the local and global neighborhood topologies for enhancing the exploration and obtaining adaptive balance between exploration and exploitation. The local neighborhoods are dynamically formed based on evolutionary states. To delineate the evolutionary states, two convergence criteria named limit value and population diversity, are introduced. Moreover, a mutation operator is designed for escaping from the local optima on the basis of evolutionary states. The proposed algorithm was evaluated on 27 benchmark problems with different characteristic and various difficulties. The results reveal that DNLGSA exhibits competitive performances when compared with a variety of state-of-the-art M-HS algorithms. Moreover, the incorporation of local neighborhood topology reduces the numbers of calculations of gravitational force and thus alleviates the high computational cost of GSA