Endothelium-dependent effect of perindopril and enalaprilat in rat thoracic aorta

AIM: To study the effect of the angiotensin-converting enzyme (ACE) inhibitors perindopril (Per) and enalaprilat (Ena) on the reactivity of the endothelium in normal rats. METHODS: Male rats were treated intragastrically with Per (2 mg.kg-1.d-1) or placebo (n = 18) for 6 wk. Aorta was isolated for experiment. Another set of isolated aortic rings with and without endothelium were incubated with Ena (0.1 mumol.L-1) for 30 min. Responses to acetylcholine, serotonin, phenylephrine, sodium nitroprusside (SN), and nitroglycerin (Nit) were observed. RESULTS: Endothelium-dependent relaxation to acetylcholine was augmented in aortic rings from rats treated with Per in comparison with control. The IC50 value (95% confidence limits) decreased from 3.8 (0.56-26.1) mumol.L-1 (control group) to 0.98 (0.28-3.41) mumol.L-1 (Per-treated group). The maximal relaxation was augmented from 62 +/- 9% to 78 +/- 10% (P < 0.01). However, the responses to the endothelium-independent vasodilators, SN and Nit, were similar. Serotonin- and phenylephrine-induced contractions were decreased, which were influenced by basal release of endothelium-derived relaxing factor (EDRF). EC50 values was 6.1 (2.6-14.4) nmol.L-1 vs 8.3 (3.6-18.8) nmol.L-1 in comparison with control group and Per-treated group. The maximal contraction was decreased from 2.42 +/- 0.29 g (control group) to 1.96 +/- 0.25 g (treated group) (P < 0.01). Similar results were found in incubation with Ena. CONCLUSION: Ena and Per enhanced the basic release of EDRF from vascular endothelium.     

Ginkgolides antagonizing some effects of platelet-activating factor in vitro

The mixture of platelet-activating factor (PAF) and platelets produced significant contraction of guinea pigs' bronchus, while the contraction induced by PAF alone was mild relatively, the IC50 were 6.14 x 10(-7) mol/L and 6.32 x 10(-4) mol/L respectively. There was significant difference between these two groups (P < 0.05). When the platelets were pre-incubated with ginkgolides for 10 minutes in Tris-Tyrode's buffered saline, effects of the PAF and platelets mixture were significantly inhibited (P < 0.01). Exposure of guinea pigs' bronchus to PAF in vitro resulted in a loss of beta-adrenergic receptors and responses to isoproterenol, and this effect of PAF was prevented by prior incubation of the guinea pigs' bronchus with ginkgolides (P < 0.05). The results showed ginkgolides were a potent PAF antagonist.     

Modeling and navigation of social information networks in metric spaces

We are living in a world of various kinds of social information networks with small-world and scale-free characteristics. It is still an intriguing problem for researchers to explain how and why so many obviously different networks emerge and share common intrinsic characteristics such as short diameter, higher cluster and power-law degree distribution. Most previous works studied the topology formation and information navigation of complex networks in separated models. In this paper, we propose a metric based range intersection model to explore the topology evolution and information navigation in a synthetic way. We model the network as a set of nodes in a distance metric space where each node has an ID and a range of neighbor information around its ID in the metric space. The range of a node can be seen as the local knowledge or information that the node has around its position in the metric space. The topology is formed by setting up a link between two nodes that have intersected ranges. Information navigation over the network is modeled as a greedy routing process using neighbor links and the distance metric. Different from previous models, we do not assume that nodes join the network one by one and set up link according to the degree distribution of existing nodes or distances between nodes. Range of node is the key factor determining the topology and navigation properties of a network. Moreover, as the ranges of nodes grow, the network evolves from a set of totally isolated nodes to a connected network. Thus, we can easily model the network evolutions in terms of the network size and the individual node information range using the range intersection model. A set of experiments shows that networks constructed using the range intersection model have the scale-free degree distribution, high cluster, short diameter, and high navigability properties that are owned by the real networks.     

Robust Video Text Detection with Morphological Filtering Enhanced MSER

Video text detection is a challenging problem, since video image background is generally complex and its subtitles often have the problems of color bleeding, fuzzy boundaries and low contrast due to vi...     

Analysis and Implementation of an Open Programmable Router Based on Forwarding and Control Element Separation

A router architecture based upon ForCES (Forwarding and Control Element Separation), which is being standardized by IETF ForCES working group, gains its competitive advantage over traditional router architectures in flexibility, programmability, and cost-effectiveness. In this paper, design and implementation of a ForCES-based router (ForTER) is illustrated. Firstly, the implementation architecture of ForTER is discussed. Then, a layered software model, which well illustrates ForCES features, is proposed. Based on the model, design and implementation of Control Element (CE) and Forwarding Element (FE) in ForTER are introduced in detail. Moreover, security for ForTER is considered and an algorithm to prevent DoS attacks is presented. Lastly, experiments of ForTER are illustrated for routing and running routing protocols, network management, DoS attack prevention, etc. The experimental results show the feasibility of the ForTER design. Consequently, the ForTER implementation basically testifies the feasibility of ForCES architecture and some IETF ForCES specifications.     

Intensity standardization simplifies brain MR image segmentation

Typically, brain MR images present significant intensity variation across patients and scanners. Consequently, training a classifier on a set of images and using it subsequently for brain segmentation may yield poor results. Adaptive iterative methods usually need to be employed to account for the variations of the particular scan. These methods are complicated, difficult to implement and often involve significant computational costs. In this paper, a simple, non-iterative method is proposed for brain MR image segmentation. Two preprocessing techniques, namely intensity-inhomogeneity-correction, and more importantly MR image intensity standardization, used prior to segmentation, play a vital role in making the MR image intensities have a tissue-specific numeric meaning, which leads us to a very simple brain tissue segmentation strategy.Vectorial scale-based fuzzy connectedness and certain morphological operations are utilized first to generate the brain intracranial mask. The fuzzy membership value of each voxel within the intracranial mask for each brain tissue is then estimated. Finally, a maximum likelihood criterion with spatial constraints taken into account is utilized in classifying all voxels in the intracranial mask into different brain tissue groups. A set of inhomogeneity corrected and intensity standardized images is utilized as a training data set. We introduce two methods to estimate fuzzy membership values. In the first method, called SMG (for simple membership based on a gaussian model), the fuzzy membership value is estimated by fitting a multivariate Gaussian model to the intensity distribution of each brain tissue whose mean intensity vector and covariance matrix are estimated and fixed from the training data sets. The second method, called SMH (for simple membership based on a histogram), estimates fuzzy membership value directly via the intensity distribution of each brain tissue obtained from the training data sets. We present several studies to evaluate the performance of these two methods based on 10 clinical MR images of normal subjects and 10 clinical MR images of Multiple Sclerosis (MS) patients. A quantitative comparison indicates that both methods have overall better accuracy than the k-nearest neighbors (kNN) method, and have much better efficiency than the Finite Mixture (FM) model-based Expectation-Maximization (EM) method. Accuracy is similar for our methods and EM method for the normal subject data sets, but much better for our methods for the patient data sets.     

可见光与红外图像融合质量评价指标分析

目的 客观评价作为图像融合的重要研究领域,是评价融合算法性能的有力工具。目前,已有几十种不同类型的评价指标,但各应用领域包括可见光与红外图像融合,仍缺少统一的选择依据。为了方便比较不同融合算法性能,提出一种客观评价指标的通用分析方法并应用于可见光与红外图像融合。方法 将可见光与红外图像基准数据集中的客观评价指标分为两类,分别是基于融合图像的评价指标与基于源图像和融合图像的评价指标。采用Kendall相关系数分析融合指标间的相关性,聚类得到指标分组;采用Borda计数排序法统计算法的综合排序,分析单一指标排序和综合排序的相关性,得到一致性较高的指标集合;采用离散系数分析指标均值随不同算法的波动程度,选择充分体现不同算法间差异的指标;综合相关性分析、一致性分析及离散系数分析,总结具有代表性的建议指标集合。结果 在13对彩色可见光与红外和8对灰度可见光与红外两组图像源中,分别统计分析不同图像融合算法的客观评价数据,得到可见光与红外图像融合的建议指标集(标准差、边缘保持度),作为融合算法性能评估的重要参考。相较于现有方法,实验覆盖20种融合算法和13种客观评价指标,并且不依赖主观评价结果。结论...     

校园网网络事件源定位技术研究

为了解决在校园网内部分网络安全事件和故障事件中只能确认攻击和事故的发生,而无法确定攻击和事故的源头这一问题.文章提出一种三步骤的校园网内网网络事件源定位模型.该模型将神经网络和证据理论相结合应用于攻击源定位.提高了校园网内网攻击定位的效率和准确性.