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In this paper, the improved Aw-Rascle model with viscosity term is developed to a new nonlinear dynamical system composed of ordinary differential equations, by using traveling wave substitution and a kind of variable substitution. The stability of the system is discussed by the method of phase plane analysis after getting the travelling-wave solutions, then the final data is simulated by Matlab to verify the analysis conclusion.

Melanoma is one of the most lethal skin malignancies in the world. Interferons (IFNs) have been also demonstrated in response to tumor cell and IFNs such as IFN-α have been used for melanoma treatment. The long chain double-stranded RNA (dsRNA) (from a variety of nonviral sources) is a potent activator of the IFN system and an inducer of cell apoptosis. Panaxadiolsaponins (PDS) is a major Panax ginseng-derived active component with known antitumor activity and immune modulation. Here, we investigated a hypothesis that the combination of PDS and total natural dsRNA (as opposed to the synthetic dsRNA) will suppress tumor growth better than the individual agents. We have evaluated the antitumor and immunostimulatory effects of the combination of natural long chain dsRNA (derived from yeast) and PDS on melanoma cell line B16 and mice xenograft model. The underlying mechanisms of growth suppression were investigated by analyzing dsRNA-activated pathways, apoptosis, and cell cycle. Natural dsRNA and PDS exert superior anticancer effects than either agent alone. Natural dsRNA and PDS combination might be a promising strategy for treating malignancies, including melanoma. [ABSTRACT FROM AUTHOR]

Background: Kaposi’s sarcoma (KS) is a vascular neoplasm characterized by the dysregulated expression of angiogenic and inflammatory cytokines. The driving force of the KS lesion, the KSHV-infected spindle cell, secretes elevated levels of vascular endothelial growth factor (VEGF), essential for KS development. However, the origin of VEGF in this tumor remains unclear. Methodology/Principal Findings: Here we report that the KSHV G protein-coupled receptor (vGPCR) upregulates VEGF in KS through an intricate paracrine mechanism. The cytokines secreted by the few vGPCR-expressing tumor cells activate in neighboring cells multiple pathways (including AKT, ERK, p38 and IKK\(\beta\)) that, in turn, converge on TSC1/2, promoting mTOR activation, HIF upregulation, and VEGF secretion. Conditioned media from vGPCR-expressing cells lead to an mTORdependent increase in HIF-1\(\alpha\) and HIF-2\(\alpha\) protein levels and VEGF upregulation. In a mouse allograft model for KS, specific inhibition of the paracrine activation of mTOR in non-vGPCR-expressing cells was sufficient to inhibit HIF upregulation in these cells, and abolished the ability of the vGPCR-expressing cells to promote tumor formation \(in\) \(vivo\). Similarly, pharmacologic inhibition of HIF in this model blocked VEGF secretion and also lead to tumor regression. Conclusions/Significance: Our findings provide a compelling explanation for how the few tumor cells expressing vGPCR can contribute to the dramatic amplification of VEGF secretion in KS, and further provide a molecular mechanism for how cytokine dysregulation in KS fuels angiogenesis and tumor development. These data further suggest that activation of HIF by vGPCR may be a vulnerable target for the treatment of patients with KS.

The research on traffic congestion control has seen rapid development in recent years. Investigating the bifurcation characteristics of traffic flow and designing control schemes for unstable bifurcation points can offer new methods for alleviating traffic congestion. This paper focuses on studying the bifurcation characteristics and nonlinear control of traffic flow based on the continuous model and the taillight effect. Firstly, the traffic flow model is transformed into a stability model suitable for branching analysis through the use of the traveling wave transform. This transformation facilitates the analysis of stability that reflects unstable traffic characteristics such as congestion. Based on this stability model, the existence condition of Hopf bifurcation is proved and some bifurcation points of the traffic system are identified. Secondly, the congestion and stability mutation behaviors near equilibrium and branching points are studied to understand the formation mechanism of traffic congestion. Finally, control schemes are designed using Chebyshev polynomial approximation and stochastic feedback control to delay or eliminate unstable bifurcation points and relieve traffic congestion. This improved traffic flow model helps explain changes in system stability through bifurcation analysis and identify unstable bifurcation points. It can also effectively manage these points by designing a feedback controller. It is beneficial for controlling sudden changes in traffic system stability behavior and mitigating traffic congestion, with important theoretical significance and practical application value. [ABSTRACT FROM AUTHOR]