Bandwidth Optimization of Coordinated Arterials Based on Group Partition Method

AbstractAn approach to the application of bandwidth-oriented signal timing has been proposed based on a group partition method for coordinated arterials. Firstly, the approach is an improved and detailed bandwidth optimization method, including detail steps to calculate upper/lower influences and relative offset for intersections, with different conditions. A window program BOTSD (Bandwidth Optimization and Time-Space Diagram) was developed to obtain optimal progression bandwidth and draw time-space diagram for an arterial. Secondly, a group partition method of coordinated arterials to get optimal bandwidth has been developed. In the case study, after calculating valid and optimal bandwidth for every possible subgroup, arterial progression bandwidth for every combination of possible subgroups can be obtained using the improved bandwidth optimization and group partition method. The results of intersection control delay and progression bandwidth for every combination of possible subgroups show that bandwidth-based solutions generally outperform delay-based solutions. Meanwhile, the signal timing plan from improved bandwidth optimization and group partition method is much better than the result from Synchro 6.0 optimization function and Messer's method

Dual sticky hierarchical Dirichlet process hidden Markov model and its application to natural language description of motions

In this paper, a new nonparametric Bayesian model called the dual sticky hierarchical Dirichlet process hidden Markov modle (HDP-HMM) is proposed for mining activities from a collection of time series data such as trajectories. All the time series data are clustered. Each cluster of time series data, corresponding to a motion pattern, is modeled by an HMM. Our model postulates a set of HMMs that share a common set of states (topics in an analogy with topic models for document processing), but have unique transition distributions. The number of HMMs and the number of topics are both automatically determined. The sticky prior avoids redundant states and makes our HDP-HMM more effective to model multimodal observations. For the application to motion trajectory modeling, topics correspond to motion activities. The learnt topics are clustered into atomic activities which are assigned predicates. We propose a Bayesian inference method to decompose a given trajectory into a sequence of atomic activities. The sources and sinks in the scene are learnt by clustering endpoints (origins and destinations of trajectories). The semantic motion regions are learnt using the points in trajectories. On combining the learnt sources and sinks, semantic motion regions, and the learnt sequences of atomic activities. the action represented by the trajectory can be described in natural language in as autometic a way as possible.The effectiveness of our dual sticky HDP-HMM is validated on several trajectory datasets. The effectiveness of the natural language descriptions for motions is demonstrated on the vehicle trajectories extracted from a traffic scene

Learning human actions by combining global dynamics and local appearance

In this paper, we address the problem of human action recognition through combining global temporal dynamics and local visual spatio-temporal appearance features. For this purpose, in the global temporal dimension, we propose to model the motion dynamics with robust linear dynamical systems (LDSs) and use the model parameters as motion descriptors. Since LDSs live in a non-Euclidean space and the descriptors are in non-vector form, we propose a shift invariant subspace angles based distance to measure the similarity between LDSs. In the local visual dimension, we construct curved spatio-temporal cuboids along the trajectories of densely sampled feature points and describe them using histograms of oriented gradients (HOG). The distance between motion sequences is computed with the Chi-Squared histogram distance in the bag-of-words framework. Finally we perform classification using the maximum margin distance learning method by combining the global dynamic distances and the local visual distances. We evaluate our approach for action recognition on five short clips data sets, namely Weizmann, KTH, UCF sports, Hollywood2 and UCF50, as well as three long continuous data sets, namely VIRAT, ADL and CRIM13. We show competitive results as compared with current state-of-the-art methods

γδT Cells Suppress Liver Fibrosis via Strong Cytolysis and Enhanced NK Cell-Mediated Cytotoxicity Against Hepatic Stellate Cells

Liver fibrosis is the excessive accumulation of extracellular matrix proteins, resulting from maladaptive wound healing responses to chronic liver injury. γδT cells are important in chronic liver injury pathogenesis and subsequent liver fibrosis; however, their role and underlying mechanisms are not fully understood. The present study aims to assess whether γδT cells contribute to liver fibrosis regression. Using a carbon tetrachloride (CCl4)-induced murine model of liver fibrosis in wild-type (WT) and γδT cell deficient (TCRδ−/−) mice, we demonstrated that γδT cells protected against liver fibrosis and exhibited strong cytotoxicity against activated hepatic stellate cells (HSCs). Further study show that chronic liver inflammation promoted hepatic γδT cells to express NKp46, which contribute to the direct killing of activated HSCs by γδT cells. Moreover, we identified that an IFNγ-producing γδT cell subset (γδT1) cells exhibited stronger cytotoxicity against activated HSCs than the IL-17-producing subset (γδT17) cells upon chronic liver injury. In addition, γδT cells promoted the anti-fibrotic ability of conventional natural killer (cNK) cells and liver-resident NK (lrNK) cells by enhancing their cytotoxicity against activated HSCs. The cell crosstalk between γδT and NK cells was shown to depend partly on co-stimulatory receptor 4-1BB (CD137) engagement. In conclusion, our data confirmed the protective effects of γδT cells, especially the γδT1 subset, by directly killing activated HSCs and increasing NK cell-mediated cytotoxicity against activated HSCs in CCl4-induced liver fibrosis, which suggest valuable therapeutic targets to treat liver fibrosis

A study of association between expression of hOGG1, VDAC1, HK-2 and cervical carcinoma

<p>Abstract</p> <p>Background</p> <p>Human 8-oguanine glycosylase 1(hOGG1), voltage-dependent anion channel 1(VDAC1), hexokinase 2(HK-2), represented the process of oxidative DNA damage, cell apoptosis and glycolysis, respectively. This study aims to explore the association between expression of hOGG1, VDAC1, HK-2 and cervical carcinoma.</p> <p>Methods</p> <p>A case-control study was conducted. 65 cervical biopsy samples consist of 20 control and 45 cases. The expression of hOGG1, VDAC1 and HK-2 were examined with immunohistochemistry(IHC), immunolabeling was evaluated with stereological cell counts.</p> <p>Results</p> <p>The data showed that the positive proportion of hOGG1 and HK-2 in the case group was higher than that of the control group (P < 0.05). Further, there was an increasing trend for the positive proportion and expression degree of hOGG1 and HK-2 from Control, Mild cervical carcinoma (MCC), Intermediate cervical carcinoma(ICC) to Severe cervical carcinoma(SCC) in order (P < 0.05). To VDAC1, the significant result was not obtained.</p> <p>Conclusions</p> <p>The results suggested that there was a close association between expression of hOGG1, HK-2 and cervical cancer. hOGG1 and HK-2 might play a key role at the early stage of cervical cancer, and the findings of hOGG1 and HK-2 should be considered as a significant biomarker at the early stage of cervical cancer.</p