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

Multi-Hamiltonian Structures on Spaces of Closed Equicentroaffine Plane Curves Associated to Higher KdV Flows

Higher KdV flows on spaces of closed equicentroaffine plane curves are studied and it is shown that the flows are described as certain multi-Hamiltonian systems on the spaces. Multi-Hamiltonian systems describing higher mKdV flows are also given on spaces of closed Euclidean plane curves via the geometric Miura transformation

Evolution of Surface Density Function in an Open Turbulent Jet Spray Flame

A three-dimensional Direct Numerical Simulation of an open turbulent jet spray flame representing a laboratory-scale burner configuration has been used to analyse the statistical behaviours of the magnitude of reaction progress variable gradient |∇c| [alternatively known as the Surface Density Function (SDF)] and the strain rates, which affect its evolution. The flame has been found to exhibit fuel-lean combustion close to the jet exit, but fuel-rich conditions have been obtained further downstream due to the evaporation of fuel droplets, which leads to the reduction in the mean value of the SDF in the downstream direction. This change in mixture composition in the axial direction has implications on the statistical behaviours of the SDF and the strain rates affecting its evolution. The mean value of dilatation rate remains positive, whereas the mean normal strain rate assumes positive values where the effects of heat release are strong but becomes negative towards both unburned and burned gas sides. The mean values of dilatation rate, normal strain rate and tangential strain rate decrease downstream of the jet exit. However, the mean behaviours of displacement speed and its components do not change significantly away from the jet exit. The mean values of normal strain rate arising from flame propagation remain positive and thus act to thicken the flame. The mean tangential strain rate due to flame propagation (alternatively the curvature stretch rate) remains negative throughout the flame at all axial locations investigated. The mean effective normal strain rate assumes positive values throughout the flame and it increases in the downstream direction for the present case, which is consistent with the reduction in the peak mean value of the SDF in the axial direction. The mean effective tangential strain rate (alternatively stretch rate) assumes negative values throughout the flame at all axial locations

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

Discovery and cardioprotective effects of the first non-peptide agonists of the G protein-coupled prokineticin receptor-1

Prokineticins are angiogenic hormones that activate two G protein-coupled receptors: PKR1 and PKR2. PKR1 has emerged as a critical mediator of cardiovascular homeostasis and cardioprotection. Identification of non-peptide PKR1 agonists that contribute to myocardial repair and collateral vessel growth hold promises for treatment of heart diseases. Through a combination of in silico studies, medicinal chemistry, and pharmacological profiling approaches, we designed, synthesized, and characterized the first PKR1 agonists, demonstrating their cardioprotective activity against myocardial infarction (MI) in mice. Based on high throughput docking protocol, 250,000 compounds were computationally screened for putative PKR1 agonistic activity, using a homology model, and 10 virtual hits were pharmacologically evaluated. One hit internalizes PKR1, increases calcium release and activates ERK and Akt kinases. Among the 30 derivatives of the hit compound, the most potent derivative, IS20, was confirmed for its selectivity and specificity through genetic gain- and loss-of-function of PKR1. Importantly, IS20 prevented cardiac lesion formation and improved cardiac function after MI in mice, promoting proliferation of cardiac progenitor cells and neovasculogenesis. The preclinical investigation of the first PKR1 agonists provides a novel approach to promote cardiac neovasculogenesis after MI

Exploring the Performance Benefits of End-to-End Path Switching

This paper explores the feasibility of improving the performance of end-to-end data transfers between different sites through path switching. Our study is focused on both the logic that controls path switching decisions and the configurations required to achieve sufficient path diversity. Specifically, we investigate two common approaches offering path diversity – multi-homing and overlay networks – and investigate their characteristics in the context of a representative wide-area testbed. We explore the end-to-end delay and loss characteristics of different paths and find that substantial improvements can potentially be achived by path switching, especially in lowering end-to-end losses. Based on this assessment, we develop a simple path-switching mechanism capable of realizing those performance improvements. Our experimental study demonstrates that substantial performance improvements are indeed achievable using this approach

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

Variability in organ-specific EGFR mutational spectra in tumour epithelium and stroma may be the biological basis for differential responses to tyrosine kinase inhibitors

Organ-specific differences in epidermal growth factor receptor (EGFR) mutational spectra and frequencies were found in lung cancer and sporadic and BRCA1/2-related breast cancers. Additionally, we found a high frequency of EGFR mutations in the tumour stroma of these invasive breast carcinomas. Those organ-specific mutational spectra and potential targets in the cancer-associated stroma might influence the efficacy of TKI therapy