Error and Attack Tolerance of Layered Complex Networks

Many complex systems may be described not by one, but by a number of complex networks mapped one on the other in a multilayer structure. The interactions and dependencies between these layers cause that what is true for a distinct single layer does not necessarily reflect well the state of the entire system. In this paper we study the robustness of three real-life examples of two-layer complex systems that come from the fields of communication (the Internet), transportation (the European railway system) and biology (the human brain). In order to cover the whole range of features specific to these systems, we focus on two extreme policies of system's response to failures, no rerouting and full rerouting. Our main finding is that multilayer systems are much more vulnerable to errors and intentional attacks than they seem to be from a single layer perspective.Comment: 5 pages, 3 figure

Fluctuation-induced traffic congestion in heterogeneous networks

In studies of complex heterogeneous networks, particularly of the Internet, significant attention was paid to analyzing network failures caused by hardware faults or overload, where the network reaction was modeled as rerouting of traffic away from failed or congested elements. Here we model another type of the network reaction to congestion -- a sharp reduction of the input traffic rate through congested routes which occurs on much shorter time scales. We consider the onset of congestion in the Internet where local mismatch between demand and capacity results in traffic losses and show that it can be described as a phase transition characterized by strong non-Gaussian loss fluctuations at a mesoscopic time scale. The fluctuations, caused by noise in input traffic, are exacerbated by the heterogeneous nature of the network manifested in a scale-free load distribution. They result in the network strongly overreacting to the first signs of congestion by significantly reducing input traffic along the communication paths where congestion is utterly negligible.Comment: 4 pages, 3 figure

Supporting User-Defined Functions on Uncertain Data

Uncertain data management has become crucial in many sensing and scientific applications. As user-defined functions (UDFs) become widely used in these applications, an important task is to capture result uncertainty for queries that evaluate UDFs on uncertain data. In this work, we provide a general framework for supporting UDFs on uncertain data. Specifically, we propose a learning approach based on Gaussian processes (GPs) to compute approximate output distributions of a UDF when evaluated on uncertain input, with guaranteed error bounds. We also devise an online algorithm to compute such output distributions, which employs a suite of optimizations to improve accuracy and performance. Our evaluation using both real-world and synthetic functions shows that our proposed GP approach can outperform the state-of-the-art sampling approach with up to two orders of magnitude improvement for a variety of UDFs. 1

Priority diffusion model in lattices and complex networks

We introduce a model for diffusion of two classes of particles ($A$ and $B$) with priority: where both species are present in the same site the motion of $A$'s takes precedence over that of $B$'s. This describes realistic situations in wireless and communication networks. In regular lattices the diffusion of the two species is normal but the $B$ particles are significantly slower, due to the presence of the $A$ particles. From the fraction of sites where the $B$ particles can move freely, which we compute analytically, we derive the diffusion coefficients of the two species. In heterogeneous networks the fraction of sites where $B$ is free decreases exponentially with the degree of the sites. This, coupled with accumulation of particles in high-degree nodes leads to trapping of the low priority particles in scale-free networks.Comment: 5 pages, 3 figure

A novel mutation in the tyrosine kinase domain of ERBB2 in hepatocellular carcinoma

BACKGROUND: Several studies showed that gain-of-function somatic mutations affecting the catalytic domain of EGFR in non-small cell lung carcinomas were associated with response to gefitinib and erlotinib, both EGFR-tyrosine kinase inhibitors. In addition, 4% of non-small cell lung carcinomas were shown to have ERBB2 mutations in the kinase domain. In our study, we sought to determine if similar respective gain-of-function EGFR and ERBB2 mutations were present in hepatoma and/or biliary cancers. METHODS: We extracted genomic DNA from 40 hepatoma (18) and biliary cancers (22) samples, and 44 adenocarcinomas of the lung, this latter as a positive control for mutation detection. We subjected those samples to PCR-based semi-automated double stranded nucleotide sequencing targeting exons 18–21 of EGFR and ERBB2. All samples were tested against matched normal DNA. RESULTS: We found 11% of hepatoma, but no biliary cancers, harbored a novel ERBB2 H878Y mutation in the activating domain. CONCLUSION: These newly described mutations may play a role in predicting response to EGFR-targeted therapy in hepatoma and their role should be explored in prospective studies

High-throughput genomic technology in research and clinical management of breast cancer. Molecular signatures of progression from benign epithelium to metastatic breast cancer

It is generally accepted that early detection of breast cancer has great impact on patient survival, emphasizing the importance of early diagnosis. In a widely recognized model of breast cancer development, tumor cells progress through chronological and well defined stages. However, the molecular basis of disease progression in breast cancer remains poorly understood. High-throughput molecular profiling techniques are excellent tools for the study of complex molecular alterations. By accurately mapping changes in the genome and subsequent biological/molecular pathways, the chances of finding potential novel treatment targets as well as intervention strategies are enhanced, and ultimately lives can be saved. This review provides a brief summary of recent progress in identifying molecular markers for invasiveness in early breast lesions

Predictions of NO and CO emissions in ammonia/methane/air combustion by LES using a non-adiabatic flamelet generated manifold

A large-eddy simulation (LES) employing a non-adiabatic flamelet generated manifold approach, which can account for the effects of heat losses due to radiation and cold walls, is applied to NH3/CH4/air combustion fields generated by a swirl burner, and the formation mechanisms of NO and CO for ammonia combustion are investigated in detail. The amounts of NO and CO emissions for various equivalence ratios, are compared with those predicted by LES employing the conventional adiabatic flamelet generated manifold approach and measured in the bespoke experiments. The results show that the amounts of NO and CO emissions predicted by the large-eddy simulations with the non-adiabatic flamelet generated manifold approach agree well with the experiments much better than the ones with the adiabatic flamelet generated manifold approach. This is because the NO and CO reactions for NH3/CH4/air combustion are quite susceptible to H and OH radicals’ concentrations and gas temperature. This suggests that it is essential to take into account the effects of various heat losses caused by radiation and cold walls in predicting the NO and CO emissions for the combustion of ammonia as a primary fuel