Rationale: Hepatocellular carcinoma (HCC) is an aggressive malignant solid tumor wherein CDK1/PDK1/β-Catenin is activated, suggesting that inhibition of this pathway may have therapeutic potential.Methods: CDK1 overexpression and clinicopathological parameters were analyzed. HCC patient-derived xenograft (PDX) tumor models were treated with RO3306 (4 mg/kg) or sorafenib (30 mg/kg), alone or in combination. The relevant signaling of CDK1/PDK1/β-Catenin was measured by western blot. Silencing of CDK1 with shRNA and corresponding inhibitors was performed for mechanism and functional studies.Results: We found that CDK1 was frequently augmented in up to 46% (18/39) of HCC tissues, which was significantly associated with poor overall survival (p=0.008). CDK1 inhibitor RO3306 in combination with sorafenib treatment significantly decreased tumor growth in PDX tumor models. Furthermore, the combinatorial treatment could overcome sorafenib resistance in the HCC case #10 PDX model. Western blot results demonstrated the combined administration resulted in synergistic down-regulation of CDK1, PDK1 and β-Catenin as well as concurrent decreases of pluripotency proteins Oct4, Sox2 and Nanog. Decreased CDK1/PDK1/β-Catenin was associated with suppression of epithelial mesenchymal transition (EMT). In addition, a low dose of RO3306 and sorafenib combination could inhibit 97H CSC growth via decreasing the S phase and promoting cells to enter into a Sub-G1 phase. Mechanistic and functional studies silencing CDK1 with shRNA and RO3306 combined with sorafenib abolished oncogenic function via downregulating CDK1, with downstream PDK1 and β-Catenin inactivation.Conclusion: Anti-CDK1 treatment can boost sorafenib antitumor responses in PDX tumor models, providing a rational combined treatment to increase sorafenib efficacy in the clinic.
Since the inception of network coding in information theory, we have witnessed a sharp increase of research interest in its applications in communications and networking, where the focus has been on more practical aspects. However, thus far, network coding has not been deployed in real-world commercial systems in operation at a large scale, and in a production setting. In this paper, we present the objectives, rationale, and design in the first production deployment of random network coding, where it has been used in the past year as the cornerstone of a large-scale production on-demand streaming system, operated by UUSee Inc., delivering thousands of on-demand video channels to millions of unique visitors each month. To achieve a thorough understanding of the performance of network coding, we have collected 200 Gigabytes worth of real-world traces throughout the 17-day Summer Olympic Games in August 2008, and present our lessons learned after an in-depth trace-driven analysis.
Secondary spectrum access is emerging as a promising approach for mitigating the spectrum scarcity in wireless networks. Coordinated spectrum access for secondary users can be achieved using periodic spectrum auctions. Recent studies on such auction design mostly neglect the repeating nature of such auctions, and focus on greedily maximizing social welfare. Such auctions can cause subsets of users to experience starvation in the long run, reducing their incentive to continue participating in the auction. It is desirable to increase the diversity of users allocated spectrum in each auction round, so that a trade-off between social welfare and fairness is maintained. We study truthful mechanisms towards this objective, for both local and global fairness criteria. For local fairness, we introduce randomization into the auction design, such that each user is guaranteed a minimum probability of being assigned spectrum. Computing an optimal, interference-free spectrum allocation is NP-Hard; we present an approximate solution, and tailor a payment scheme to guarantee truthful bidding is a dominant strategy for all secondary users. For global fairness, we adopt the classic maxmin fairness criterion. We tailor another auction by applying linear programming techniques for striking the balance between social welfare and max-min fairness, and for finding feasible channel allocations. In particular, a pair of primal and dual linear programs are utilized to guide the probabilistic selection of feasible allocations towards a desired tradeoff in expectation.
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