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

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

A hop-count based positioning algorithm for wireless ad-hoc networks

We propose a range-free localization algorithm for a wireless ad-hoc network utilizing the hop-count metric’s ability to indicate proximity to anchors (i.e., nodes with known positions). In traditional sense, hop-count generally means the number of intermediate routers a datagram has to go through between its source and the destination node. We analytically show that hop-count could be used to indicate proximity relative to an anchor node. Our proposed algorithm is computationally feasible for resource constrained wireless ad-hoc nodes, and gives reasonable accuracy. We perform both real experiments and simulations to evaluate the algorithm’s performance. Experimental results show that our algorithm outperforms similar proximity based algorithms utilizing received signal strength and expected transmission count. We also analyze the impact of various parameters like the number of anchor nodes, placements of anchor nodes and varying transmission powers of the nodes on the hop-count based localization algorithm’s performance through simulation

Acute Pancreatitis due to pH-Dependent Mesalazine That Occurred in the Course of Ulcerative Colitis

We report the case of a 26-year-old male who presented with acute pancreatitis during the course of treatment for pancolitic ulcerative colitis (UC) with a time-dependent mesalazine formulation, prednisolone and azathioprine (AZA). Despite a review of his clinical history and various tests, the cause of pancreatitis could not be determined. Since drug-induced pancreatitis was considered possible, administration of the time-dependent mesalazine preparation and AZA was discontinued, and conservative treatment for acute pancreatitis was performed. The pancreatitis promptly improved with these treatments, but drug lymphocyte stimulation test (DLST) for both the time-dependent mesalazine formulation and AZA was negative. A pH-dependent mesalazine formulation was given for maintenance therapy of UC. Subsequently, as the pancreatitis relapsed, drug-induced pancreatitis was strongly suspected. Administration of mesalazine was discontinued, and pancreatitis was smoothly in remission by conservative treatment. According to the positive DLST result for the pH-dependent mesalazine formulation and the clinical course, a diagnosis of pH-dependent mesalazine-induced pancreatitis due to this formulation was made. During the clinical course of UC, occurrence of drug-induced pancreatitis must always be considered

Serum galectin-9 levels are elevated in the patients with type 2 diabetes and chronic kidney disease

Background: Galectin-9 (Gal-9) induces apoptosis in activated T helper 1 (T(H)1) cells as a ligand for T cell immunoglobulin mucin-3 (Tim-3). Gal-9 also inhibits the G1 phase cell cycle arrest and hypertrophy in db/db mice, the hallmark of early diabetic nephropathy, by reversing the high glucose-induced up-regulation of cyclin dependent kinase inhibitors such as p27(Kip1) and p21(Cip1). Methods: We investigated the serum levels of Gal-9 in the patients with type 2 diabetes and various stages of chronic kidney disease (CKD) (n = 182). Results: Serum Gal-9 levels in the patients with type 2 diabetes were 131.9 +/- 105.4 pg/ml and Log(10)Gal-9 levels significantly and positively correlated with age (r = 0.227, p = 0.002), creatinine (r = 0.175, p = 0.018), urea nitrogen (r = 0.162, p = 0.028) and osmotic pressure (r = 0.187, p = 0.014) and negatively correlated with estimated glomerular filtration rate (eGFR) (r = -0.188, p = 0.011). Log(10)Gal-9 levels increased along with the progression of GFR categories of G1 to G4, and they were statistically significant by Jonckheere-Terpstra test (p = 0.012). Log(10)Gal-9 levels remained similar levels in albuminuria stages of A1 to A3. Conclusion: The elevation of serum Gal-9 in the patients with type 2 diabetes is closely linked to GFR and they may be related to the alteration of the immune response and inflammation of the patients with type 2 diabetes and CKD

Identification of Molecular Distinctions Between Normal Breast-Associated Fibroblasts and Breast Cancer-Associated Fibroblasts

Stromal fibroblasts influence the behavior of breast epithelial cells. Fibroblasts derived from normal breast (NAF) inhibit epithelial growth, whereas fibroblasts from breast carcinomas (CAF) have less growth inhibitory capacity and can promote epithelial growth. We sought to identify molecules that are differentially expressed in NAF versus CAF and potentially responsible for their different growth regulatory abilities. To determine the contribution of soluble molecules to fibroblast–epithelial interactions, NAF were grown in 3D, transwell or direct contact co-cultures with MCF10AT epithelial cells. NAF suppressed proliferation of MCF10AT in both direct contact and transwell co-cultures, but this suppression was significantly greater in direct co-cultures, indicating involvement of both soluble and contact factors. Gene expression profiling of early passage fibroblast cultures identified 420 genes that were differentially expressed in NAF versus CAF. Of the eight genes selected for validation by real-time PCR, FIBULIN 1, was overexpressed in NAF, and DICKKOPF 1, NEUREGULIN 1, PLASMINOGEN ACTIVATOR INHIBITOR 2, and TISSUE PLASMINOGEN ACTIVATOR were overexpressed in CAF. A higher expression of FIBULIN 1 in normal- than cancer-associated fibroblastic stroma was confirmed by immunohistochemistry of breast tissues. Among breast cancers, stromal expression of Fibulin 1 protein was higher in estrogen receptor α-positive cancers and low stromal expression of Fibulin 1 correlated with a higher proliferation of cancer epithelial cells. In conclusion, expression profiling of NAF and CAF cultures identified many genes with potential relevance to fibroblast–epithelial interactions in breast cancer. Furthermore, these early passage fibroblast cultures can be representative of gene expression in stromal fibroblasts in vivo