| 1. |
- Sun, Xu, et al.
(författare)
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Elevated heparanase expression is associated with poor prognosis in breast cancer : a study based on systematic review and TCGA data
- 2017
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Ingår i: Oncotarget. - : IMPACT JOURNALS LLC. - 1949-2553. ; 8:26, s. 43521-43535
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Forskningsöversikt (refereegranskat)abstract
- Heparanase promotes tumorigenesis, angiogenesis, and metastasis. Here, we conducted a study based on systematic review and the Cancer Genome Atlas (TCGA) data that examined heparanase expression in clinical samples to determine its prognostic value. According to the meta-analysis and TCGA data, we found that heparanase expression was up-regulated in most breast cancer specimens, and elevated heparanase expression was associated with increased lymph node metastasis, larger tumor size, higher histological grade, and poor survival. These results suggest that targeting heparanase might improve treatments for breast cancer patients.
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| 2. |
- Han, Q., et al.
(författare)
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Backhaul-Aware User Association and Resource Allocation for Energy-Constrained HetNets
- 2017
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Ingår i: IEEE Transactions on Vehicular Technology. - : IEEE Press. - 0018-9545 .- 1939-9359. ; 66:1, s. 580-593
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Tidskriftsartikel (refereegranskat)abstract
- Growing attention has been paid to renewable-or hybrid-energy-powered heterogeneous networks (HetNets). In this paper, focusing on backhaul-aware joint user association and resource allocation for this type of HetNets, we formulate an online optimization problem to maximize the network utility reflecting proportional fairness. Since user association and resource allocation are tightly coupled not only on resource consumption of the base stations (BSs) but in the constraints of their available energy and backhaul as well, the closed-form solution is quite difficult to obtain. Thus, we solve the problem distributively by employing certain decomposition methods. Specifically, at first, by adopting the primal decomposition method, we decompose the original problem into a lower level resource-allocation problem for each BS and a higher level user-association problem. For the optimal resource allocation, we prove that a BS either assigns equal normalized resources or provides an equal long-term service rate to its served users. Then, the user-association problem is solved by the Lagrange dual decomposition method, and a completely distributed algorithm is developed. Moreover, applying results of the subgradient method, we demonstrate the convergence of the proposed distributed algorithm. Furthermore, to efficiently and reliably apply the proposed algorithm to the future wireless networks with an extremely dense BS deployment, we design a virtual user association and resource allocation scheme based on the software-defined networking architecture. Finally, numerical results validate the convergence of the proposed algorithm and the significant improvement on network utility, load balancing, and user fairness.
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| 3. |
- Wu, J., et al.
(författare)
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Traffic-Aware Data and Signaling Resource Management for Green Cellular Networks
- 2014
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Konferensbidrag (refereegranskat)abstract
- The increasing traffic demands bring heavy load toboth the data and control planes of cellular networks, along withsubstantial energy consumption. To solve the issue, new networkarchitecture that separates signaling and data has been proposedin literature for future green cellular networks. In this paper, weanalyze the data and signaling resource configuration problemin this new network architecture. We find the optimal resourcepartitioning parameters to optimize the blocking performanceand to minimize the overall network power consumption witha blocking probability constraint. More specifically, we adopttraffic-aware resource allocation between the data and signalingbase stations (BSs) to improve network access capability whilereducing the overall network power consumption. Two typesof resource partitioning patterns, complete partitioning andpartial partitioning, are studied. Numerical results show thatgreat energy-saving gain can be achieved compared with thetraditional fixed and traffic-proportional resource partitioningpatterns. Moreover, power consumption and blocking performancetradeoffs are explored, based on which the appropriateresource partitioning pattern can be chosen according to differentquality of service (QoS) requirements.
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