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

Compositional and Functional Changes in Microbial Communities of Composts Due to the Composting-Related Factors and the Presence of \u3ci\u3eListeria monocytogenes\u3c/i\u3e

Listeria monocytogenes is a leading foodborne pathogen that can contaminate fresh produce in farm environment, resulting in deadly outbreaks. Composts contain a diversity of microorganisms, and some of them may be compost-adapted competitive exclusion microorganisms against L. monocytogenes. To understand interactions between compost microflora and the pathogen, both dairy- and poultry-wastes based composts (n = 12) were inoculated with L. monocytogenes, and then analyzed by next-generation sequencing approaches along with culturing methods. DNA extraction and enumeration of L. monocytogenes were performed at 0 and 72 h post-incubation at room temperature. The major bacterial phyla were identified as Firmicutes (23%), Proteobacteria (23%), Actinobacteria (19%), Chloroflexi (13%), Bacteroidetes (12%), Gemmatimonadetes (2%), and Acidobacteria (2%). The top three indicator genera enriched in different compost types were identified by LEfSe with LDA score . 2. The interactions between L. monocytogenes and indigenous microflora were limited as no significant changes in the dominant microbial members in compost ecosystem, but some discriminatory species such as Bacillus, Geobacillus, and Brevibacterium were identified by Random Forest analysis. Besides, changes in metabolic pathways and the increased abundance of bacteriocins category in the compost samples containing L. monocytogenes after 72 h postinoculation were revealed by metatranscriptomic sequencing. Taken together, the compost-related factors such as compost types, composting stages, and the collection farms are major drivers that affect compost microbial compositions, and the analysis of compost metagenome implied that interactions between L. monocytogenes and compost microflora may include competition for nutrients and the presence of antimicrobials

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

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

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

What Determine China’s Inflation?

We examine determinants of inflation in China. Analyses of both yearonyear and monthonmonth growth data confirm excess liquidity, output gap, housing prices and stock prices positively affecting inflation. Impulse response analyses indicate that most effects occur during the initial five months and disappear after 10 months. Effects of real interest rates and exchange rates on inflation are relatively weak. Our results suggest that output gap is as important as excess liquidity in explaining inflation trajectory. The central bank should closely monitor asset prices given their spillovers to inflation. Currently liquidity measures are still central for controlling inflation, but further liberalization of interest rates and exchange rates are critical.China, inflation, excess liquidity

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

Effects of soil flooding on photosynthesis and growth of Zea mays L. seedlings under different light intensities

Soil flooding is one of the major abiotic stresses that repress maize (Zea mays L.) growth and yield, and other environmental factors often influence soil flooding stress. This paper reports an experimental test of the hypothesis that light intensity can influence the responses of maize seedlings to soil flooding. In this experiment, maize seedlings were subjected to soil flooding at the two-leaf stage under control light (600 μmol m-2 s-1) or low light (150 μmol m-2 s-1) conditions. Under control light growth conditions, the average photosynthetic rate (PN), transpiration rate (E) and water use efficiency (WUE) were 70, 26 and 59%, respectively, higher in non-flooded than in flooded seedlings; and the average chlorophyll a (Chl a), chlorophyll b (Chl b) and Chl a+b were 31, 42 and 34%, respectively, higher in non-flooded than in flooded seedlings; and the average belowground biomass and total biomass were 52 and 34%, respectively, higher in non-flooded than in flooded seedlings. There was a slight decrease of seedling biomass in six days flooded seedlings under low light growth conditions. The effects of flooding on photosynthetic, seedling growth and shoot/root ratio were more pronounced under control light growth conditions than under low light growth conditions, which indicate that even for maize which is a C4 plant, relatively high light intensity still aggravated soil flooding stress, while low light growth condition mitigated soil flooding stress, and suggests that light effects should be considered when we study maize responses to soil flooding.Keywords: Biomass accumulation, gas exchange, light limitation, maize, stres

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