The axillary lymph node status remains the most valuable prognostic factor for breast cancer patients. However, approximately 20-30% of node-positive patients remain free of distant metastases within 15-30 years. It is important to develop molecular markers that are able to predict for the risk of distant metastasis and to develop patient-tailored therapy strategies. We hypothesize that the lymph node metastases may represent the most metastatic fraction of the primary cancers. Therefore, we sought to identify the differentially expressed genes by microarray between the primary tumors and their paired lymph node metastases samples collected from 26 patients. A set of 79 differentially expressed genes between primary cancers and metastasis samples was identified to correctly separate most of primary cancers from lymph node metastases. And decreased expression of matrix metalloproteinase 2, fibronectin, osteoblast specific factor 2, collagen type XI alpha 1 in lymph node metastases were further confirmed by real-time RT-PCR performed on 30 specimen pairs. This set of genes also classified 35 primary cancers into two groups with different prognosis: "high risk group" and "low risk group." Patients in "high risk group" had a 4.65-fold hazard ratio (95% CI 1.02-21.13, P = 0.047) to develop a distant metastasis within 43 months comparing with the "low risk group." This suggested that the gene signature consisting of 79 differentially expressed genes between primary cancers and lymph node metastases could also predict clinical outcome of node-positive patients, and that the molecular classification based on the gene signature could guide patient-tailored therapy.
Fat trees are considered suitable structures for data center interconnection networking. Such structures are rigid, and hard to scale up and scale out. A good data center network structure should have high scalability, efficient switch utilization, and high reliability. In this paper we present a class of data center network structures based on hypergraph theory and combinatorial block design theory. We show that our data center network structures are more flexible and scalable than fat trees. Using switches of the same size, our data center network structures can connect more nodes than fat trees, and it is possible to construct different structures with tradeoffs among inter-cluster communication capacity, reliability, the number of switches used, and the number of connected nodes.
Antenna arrays are able to improve the directivity performance and reduce the cost of wireless communication systems. However, how to reduce the maximum sidelobe level (SLL) of the beam pattern is a key problem in antenna arrays. In this paper, three kinds of antenna arrays that are linear antenna array (LAA), circular antenna array (CAA) and random antenna array (RAA) are investigated. First, we formulate the SLL suppression optimization problems of LAA, CAA and RAA, respectively. Then, we propose a novel method called improved chicken swarm optimization (ICSO) approach to solve the formulated optimization problems. ICSO introduces four enhanced strategies including the local search factor, weighting factor and global search factor into the update method of conventional chicken swarm optimization (CSO) algorithm, respectively, for achieving better beam pattern optimization results of antenna arrays. Moreover, a variation mechanism is proposed to enhance the population diversity so that further improving the performance of the algorithm. We conduct simulations to evaluate the performance of the proposed ICSO for the maximum SLL suppressions of LAAs, CAAs and RAAs, and the results show that ICSO obtains lower maximum SLLs for different antenna array cases with different numbers of antenna elements compared to several other algorithms.
SummaryNetwork traffic classification is a fundamental research topic on high-performance network protocol design and network operation management. Compared with other state-of-the-art studies done on the network traffic classification, machine learning (ML) methods are more flexible and intelligent, which can automatically search for and describe useful structural patterns in a supplied traffic dataset. As a typical ML method, support vector machines (SVMs) based on statistical theory has high classification accuracy and stability. However, the performance of SVM classifier can be severely affected by the data scale, feature dimension, and parameters of the classifier. In this paper, a real-time accurate SVM training model named SPP-SVM is proposed. An SPP-SVM is deducted from the scaling dataset and employs principal component analysis (PCA) to extract data features and verify its relevant traffic features obtained from PCA. By employing PCA algorithm to do the dimension extraction, SPP-SVM confirms the critical component features, reduces the redundancy among them, and lowers the original feature dimension so as to reduce the over fitting and increase its generalization effectively. The optimal working parameters of kernel function used in SPP-SVM are derived automatically from improved particle swarm optimization algorithm, which will optimize the global solution and make its inertia weight coefficient adaptive without searching for the parameters in a wide range, traversing all the parameter points in the grid and adjusting steps gradually. The performance of its two-and multi-class classifiers is proved over 2 sets of traffic traces, coming from different topological points on the Internet. Experiments show that the SPP-SVM's two-and multi-class classifiers are superior to the typical supervised ML algorithms and performs significantly better than traditional SVM in classification accuracy, dimension, and elapsed time.
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