On Node Isolation under Churn in Unstructured P2P Networks with Heavy-Tailed Lifetimes

Previous analytical studies [12], [18] of unstructured P2P resilience have assumed exponential user lifetimes and only considered age-independent neighbor replacement. In this paper, we overcome these limitations by introducing a general node-isolation model for heavy-tailed user lifetimes and arbitrary neighbor-selection algorithms. Using this model, we analyze two age-biased neighbor-selection strategies and show that they significantly improve the residual lifetimes of chosen users, which dramatically reduces the probability of user isolation and graph partitioning compared to uniform selection of neighbors. In fact, the second strategy based on random walks on age-weighted graphs demonstrates that for lifetimes with infinite variance, the system monotonically increases its resilience as its age and size grow. Specifically, we show that the probability of isolation converges to zero as these two metrics tend to infinity. We finish the paper with simulations in finite-size graphs that demonstrate the effect of this result in practice

Residual-Based Estimation of Peer and Link Lifetimes in P2P Networks

Existing methods of measuring lifetimes in P2P systems usually rely on the so-called Create-BasedMethod (CBM), which divides a given observation window into two halves and samples users ldquocreatedrdquo in the first half every Delta time units until they die or the observation period ends. Despite its frequent use, this approach has no rigorous accuracy or overhead analysis in the literature. To shed more light on its performance, we first derive a model for CBM and show that small window size or large Delta may lead to highly inaccurate lifetime distributions. We then show that create-based sampling exhibits an inherent tradeoff between overhead and accuracy, which does not allow any fundamental improvement to the method. Instead, we propose a completely different approach for sampling user dynamics that keeps track of only residual lifetimes of peers and uses a simple renewal-process model to recover the actual lifetimes from the observed residuals. Our analysis indicates that for reasonably large systems, the proposed method can reduce bandwidth consumption by several orders of magnitude compared to prior approaches while simultaneously achieving higher accuracy. We finish the paper by implementing a two-tier Gnutella network crawler equipped with the proposed sampling method and obtain the distribution of ultrapeer lifetimes in a network of 6.4 million users and 60 million links. Our experimental results show that ultrapeer lifetimes are Pareto with shape alpha ap 1.1; however, link lifetimes exhibit much lighter tails with alpha ap 1.8

Residual-Based Measurement of Peer and Link Lifetimes in Gnutella Networks

Existing methods of measuring lifetimes in P2P systems usually rely on the so-called create-based method (CBM), which divides a given observation window into two halves and samples users created in the first half every Delta time units until they die or the observation period ends. Despite its frequent use, this approach has no rigorous accuracy or overhead analysis in the literature. To shed more light on its performance, we flrst derive a model for CBM and show that small window size or large Delta may lead to highly inaccurate lifetime distributions. We then show that create-based sampling exhibits an inherent tradeoff between overhead and accuracy, which does not allow any fundamental improvement to the method. Instead, we propose a completely different approach for sampling user dynamics that keeps track of only residual lifetimes of peers and uses a simple renewal-process model to recover the actual lifetimes from the observed residuals. Our analysis indicates that for reasonably large systems, the proposed method can reduce bandwidth consumption by several orders of magnitude compared to prior approaches while simultaneously achieving higher accuracy. We finish the paper by implementing a two-tier Gnutella network crawler equipped with the proposed sampling method and obtain the distribution of ultrapeer lifetimes in a network of 6.4 million users and 60 million links. Our experimental results show that ultrapeer lifetimes are Pareto with shape a alpha ap 1.1; however, link lifetimes exhibit much lighter tails with alpha ap 1.9

Classification Based on Pruning and Double Covered Rule Sets for the Internet of Things Applications

The Internet of things (IOT) is a hot issue in recent years. It accumulates large amounts of data by IOT users, which is a great challenge to mining useful knowledge from IOT. Classification is an effective strategy which can predict the need of users in IOT. However, many traditional rule-based classifiers cannot guarantee that all instances can be covered by at least two classification rules. Thus, these algorithms cannot achieve high accuracy in some datasets. In this paper, we propose a new rule-based classification, CDCR-P (Classification based on the Pruning and Double Covered Rule sets). CDCR-P can induce two different rule sets A and B. Every instance in training set can be covered by at least one rule not only in rule set A, but also in rule set B. In order to improve the quality of rule set B, we take measure to prune the length of rules in rule set B. Our experimental results indicate that, CDCR-P not only is feasible, but also it can achieve high accuracy

Load flow calculation for droop-controlled islanded microgrids based on direct Newton-Raphson method with step size optimisation

Load flow calculation for droop-controlled islanded microgrids (IMGs) is different from that of transmission or distribution systems due to the absence of slack bus and the variation of frequency. Meanwhile considering the common three-phase imbalance condition in low-voltage systems, a load flow algorithm based on the direct Newton-Raphson (NR) method with step size optimisation for both three-phase balanced and unbalanced droop-controlled IMGs is proposed in this study. First, the steady-state models for balanced and unbalanced droop-controlled IMGs are established based on their operational mechanisms. Then taking frequency as one of the unknowns, the non-linear load flow equations are solved iteratively by the NR method. Generally, iterative load flow algorithms are faced with challenges of convergence performance, especially for unbalanced systems. To tackle this problem, a step-size-optimisation scheme is employed to improve the convergence performance for three-phase unbalanced IMGs. In each iteration, a multiplier is deduced from the sum of higher-order terms of Taylor expansion of the load flow equations. Then the step size is optimised by the multiplier, which can help smooth the iterative process and obtain the solutions. The proposed method is performed on several balanced and unbalanced IMGs. Numerical results demonstrate the correctness and effectiveness of the proposed algorithm

RNF115/BCA2 E3 Ubiquitin Ligase Promotes Breast Cancer Cell Proliferation through Targeting p21Waf1/Cip1 for Ubiquitin-Mediated Degradation

AbstractThe E3 ubiquitin ligase RING finger protein 115 (RNF115), also known as breast cancer-associated gene 2 (BCA2), has previously been reported to be overexpressed in estrogen receptor α (ERα)-positive breast tumors and to promote breast cell proliferation; however, its mechanism is unknown. In this study, we demonstrated that silencing of BCA2 by small interfering RNAs (siRNAs) in two ERα-positive breast cancer cell lines, MCF-7 and T47D, decreases cell proliferation and increases the protein levels of the cyclin-dependent kinase inhibitor p21Waf/Cip1. The protein stability of p21 was negatively regulated by BCA2. BCA2 directly interacts with p21 and promotes p21 ubiquitination and proteasomal degradation. Knockdown of p21 partially rescues the cell growth arrest induced by the BCA2 siRNA. These results suggest that BCA2 promotes ERα-positive breast cancer cell proliferation at least partially through downregulating the expression of p21

Unifying Models of Churn and Resilience for Unstructured P2P Graphs

Previous analytical results on the resilience of unstructured P2P systems have not explicitly modeled heterogeneity of user churn (i.e, difference in online behavior) or the impact of in-degree on system resilience. To overcome these limitations, we introduce a unifying model of heterogeneous user churn and derive the distribution of the various metrics observed in prior experimental studies. We also show that the arrival process of in-edges to each user converges to Poisson when system size tends to infinity, model transient behavior of in-degree, and apply these results to obtain the joint in/out-degree isolation probability

Potent anti-tumor effects of a dual specific oncolytic adenovirus expressing apoptin in vitro and in vivo

<p>Abstract</p> <p>Background</p> <p>Oncolytic virotherapy is an attractive drug platform of cancer gene therapy, but efficacy and specificity are important prerequisites for success of such strategies. Previous studies determined that Apoptin is a p53 independent, bcl-2 insensitive apoptotic protein with the ability to specifically induce apoptosis in tumor cells. Here, we generated a conditional replication-competent adenovirus (CRCA), designated Ad-hTERT-E1a-Apoptin, and investigated the effectiveness of the CRCA a gene therapy agent for further clinical trials.</p> <p>Results</p> <p>The observation that infection with Ad-hTERT-E1a-Apoptin significantly inhibited growth of the melanoma cells, protecting normal human epidermal melanocytes from growth inhibition confirmed cancer cell selective adenoviral replication, growth inhibition, and apoptosis induction of this therapeutic approach. The <it>in vivo </it>assays performed by using C57BL/6 mice containing established primary or metastatic tumors expanded the <it>in vitro </it>studies. When treated with Ad-hTERT-E1a-Apoptin, the subcutaneous primary tumor volume reduction was not only observed in intratumoral injection group but in systemic delivery mice. In the lung metastasis model, Ad-hTERT-E1a-Apoptin effectively suppressed pulmonary metastatic lesions. Furthermore, treatment of primary and metastatic models with Ad-hTERT-E1a-Apoptin increased mice survival.</p> <p>Conclusions</p> <p>These data further reinforce the previously research showing that an adenovirus expressing Apoptin is more effective and advocate the potential applications of Ad-hTERT-E1a-Apoptin in the treatment of neoplastic diseases in future clinical trials.</p

Classification Based on Attribute Positive Correlation and Average Similarity of Nearest Neighbors

Abstract: The K-Nearest Neighbor algorithm (KNN) is a method for classifying objects based on the k closest training objects. An object is classified by a majority vote of its nearest neighbors. &quot;Closeness&quot; is defined in terms of the similarity measure between two objects. KNN is not only simple, but also sometimes has high accuracy. However, the quality of KNN classification result depends on the similarity measure between two objects and the selection of k. Moreover, the average similarity of the majority nearest neighbors may be less than the one of the minority nearest neighbors. To deal with these problems, in this study, we propose a new classification approach called APCAS: classification based on the attribute values which are positively correlated with one of the class labels and the average similarity of the nearest neighbors in each class. First, we define a new similarity measure based on the attribute values which are positively correlated with one of the class labels. Second, we classify a new object using the average similarity of the nearest neighbors in each class without selecting k. Experimental results on the mushroom data show that APCAS achieves high accuracy