Using 2-node hypergraph clustering coefficients to analyze disease-gene networks

We describe a class of new algorithms to construct bipartite networks that preserves a prescribed degree and joint-degree (degree-degree) distribution of the nodes. Bipartite networks are graphs that can represent real-world interactions between two disjoint sets, such as actor-movie networks, author-article networks, co-occurrence networks, and heterosexual partnership networks. Often there is a strong correlation between the degree of a node and the degrees of the neighbors of that node that must be preserved when generating a network that reflects the structure of the underling system. Our bipartite 2K (B2K) algorithms generate an ensemble of networks that preserve prescribed degree sequences for the two disjoint set of nodes in the bipartite network, and the joint-degree distribution that is the distribution of the degrees of all neighbors of nodes with the same degree. We illustrate the effectiveness of the algorithms on a romance network using the NetworkX software environment to compare other properties of a target network that are not directly enforced by the B2K algorithms. We observe that when average degree of nodes is low, as is the case for romance and heterosexual partnership networks, then the B2K networks tend to preserve additional properties, such as the cluster coefficients, than algorithms that do not preserve the joint-degree distribution of the original network.

This paper focuses on the synchronization for the delayed bipartite networks with non-delay and delay couplings. Different from single networks, the nodes of bipartite networks can be divided into two disjoint nonempty sets according to their properties or dynamics characteristics, and the nodes in different sets can be connected by edges while the nodes in the same set cannot be connected. In this bipartite network model, the node and coupling delays are simultaneously considered, and both the non-delay and delay coupling matrices are undirected and weighed. Furthermore, the intermittent control method used in this paper can be aperiodic while in most previous works are periodic. Based on the Lyapunov function method, block multiplication of matrices and other mathematical techniques, several synchronization protocols are rigorously induced. Finally, the proposed control method is evaluated by utilizing numerical simulation.

Community detection plays a crucial role in many complex networks, including the increasingly important class of bipartite networks. Modularity-based community detection algorithms for bipartite networks are hampered by their well known resolution limit. Unfortunately, the high-performing random walk based algorithm Infomap, which does not have the same constraint, cannot be applied to bipartite networks.To overcome this we integrate the projection method for bipartite networks based on common neighbors similarity into Infomap, to acquire a weighted one mode network that can be clustered by the random walks technique. We also compare results obtained from this process with results in the literature. We illustrate the proposed method on four real bipartite networks, showing that the random walks technique is more effective than the modularity technique in finding communities from bipartite networks as well.Keywordsbipartite graphcommunity detectionrandom walks

Discovering the relationships of gene to gene, gene to its related diseases, and diseases implicated in common genes is important. However, traditional biological methods can be expensive. Here, we show that the diseases implicated in common genes and the genes related to a multiple-gene disease can be inferred by the vertex similarity measures, a type of method to find the similar vertices in a network based on its structure. The relationship among diseases and the relationship among genes are modeled as two biological networks: human disease network and disease gene network. We apply the vertex similarity among the vertices in the human disease network to infer the diseases implicated in common genes. By similar manner, we utilize vertex similarity measures on the disease gene network to infer the genes related to a common multiple-gene disease. Experimental results demonstrate the potential of vertex similarity as an inexpensive approach to infer the possible links between genes and between diseases. We also develop a system to visualize and get a better understanding about the relationships among diseases and genes.

Effects of the bipartite structure of a network on performance of recommenders

CLUSTERING EFFECT OF USER-OBJECT BIPARTITE NETWORK ON PERSONALIZED RECOMMENDATION

Complex networks and economics

In this research, seven diseases OB (Obesity), SC (Stomach Cancer), CC (Colorectal Cancer), PC (Pancreatic Cancer), PRC (Prostate Cancer), LK (Leukemia), MD (Metabolic Disorder) has been encountered to analyze the network. When one or few diseases among them affects human there will be a possibility for them to be affected by the other diseases. Previous studies indicate that there are large numbers of similar biological and genetic characteristics among the seven disease. Common gene network models among these diseases have been explored for this reason. The genes obtained were compared and number of the gene has been decreased by the preprocessing and filtering process. After the filtering process, 9 common genes among seven related disease has been extracted. Topological properties, protein interactions network, enrichment analysis, co-expression network analysis, gene regulatory network, and physical interaction network are analyzed in this research. The substantial hub proteins based on biological, genetic and biochemical relationships among common genes has been identified by this analysis. Drug signature suggestion implies the 9 hub proteins which has been identified by PPI, GRN, gene interaction protein network. Finally, Protein chemical interaction and gene-disease interaction network indicated 6 significant genes and 9 significant genes consecutively for drug design. Network Enrichment shows the 9 common hub proteins interaction with other related genes. This analysis helps us to identify drug design pathway with chemical interactions. By applying further examining and studies those gene structures, it may be able to notify us to take precautionary steps to reduce the risk of Stomach Cancer and Associated disease.

Proximity measures for link prediction based on temporal events

Small-world phenomenon can occur in coupled dynamical systems which are highly clustered at a local level and yet strongly coupled at the global level. We show that cellular neural networks (CNN's) can exhibit "small-world phenomenon". We generalize the "characteristic path length" from previous works on "small-world phenomenon" into a "characteristic coupling strength" for measuring the average coupling strength of the outputs of CNN's. We also provide a simplified algorithm for calculating the "characteristic coupling strength" with a reasonable amount of computing time. We define a "clustering coefficient" and show how it can be calculated by a horizontal "hole detection" CNN, followed by a vertical "hole detection" CNN. Evolutions of the game-of-life CNN with different initial conditions are used to illustrate the emergence of a "small-world phenomenon". Our results show that the well-known game-of-life CNN is not a small-world network. However, generalized CNN life games whose individuals have strong mobility and high survival rate can exhibit small-world phenomenon in a robust way. Our simulations confirm the conjecture that a population with a strong mobility is more likely to qualify as a small world. CNN games whose individuals have weak mobility can also exhibit a small-world phenomenon under a proper choice of initial conditions. However, the resulting small worlds depend strongly on the initial conditions, and are therefore not robust.

The problem of community detection has become highly relevant due to the growing interest in social networks. The information contained in a social network is often represented as a graph. The idea of graph partitioning of graph theory can be apply to split a graph into node groups based on its topology information. In this paper the problem of detecting communities within a social network is handled applying graph clustering algorithms based on this idea. The new approach proposed is based on a genetic algorithm. A new fitness function has been designed to guide the clustering process combining different measures of network topology (Density, Centralization, Heterogeneity, Neighbourhood, Clustering Coefficient). These different network measures have been experimentally tested using a real-world social network. Experimental results show that the proposed approach is able to detect communities and the results obtained in previous work have been improved.

Recently, large-scale distributed multiagent systems consisting of one million of agents have been developed. When agents are distributed among multiple servers, both the computational and interaction cost of servers must be considered when optimizing the performance of the entire system. Multiagent systems reflect the structure of social communities and artificial networks such as the Internet. Since the networks possess characteristics common to the ‘small world’ phenomenon, networks of agents on the systems can be considered as small worlds. In that case, communities, which are the sets of agents that frequently interact with each other, exist in the network. Most previous works evaluate agents one by one to select the most appropriate agent to be moved to a different server. If the networks of agents are highly clustered, previous works divide the communities when moving agents. Since agents in the same community often interact with each other, this division of communities increases the interaction cost among servers. We propose community-based load balancing (CLB), which evaluates the communities to select the most appropriate set of agents to be moved. We conducted simulations to evaluate our proposed method according to the network of agents. Our simulations show that when the clustering coefficient is close to 1.0, the interaction cost with CLB can be approximately 30% lower than that with previous works.Keywordsmobile agentsscalability and performance issues: robustnessfault tolerance and dependability

Background: The introduction of any infectious agent into an industrial or subsistence farm worries agribusiness owners in Brazil because it reduces product quality and increases treatment costs, although most diseases are untreatable, thus causing economic losses with morbidity and mortality. Therefore, an epidemiological survey of viral diseases associated with poultry was developed by performing a detailed description of the risk factors that may be related to existing diseases using domestic poultry sample data recorded in the Regional Diagnostic Laboratory (LRD) of College of Veterinary Medicine of the Federal University of Pelotas (UFPel), Rio Grande do Sul, Brazil, from 2000 to 2016.Materials, Methods & Results: Epidemiological and clinical-pathological data were collected and then compared with disease data by multivariate analysis using statistical EpiInfo version 6.04 and Microsoft Office Excel 2010 software. The frequencies and 95% confidence intervals (CI), association measures (odds ratio=OR and relative risk=RR), Chi-square test, and the results considered significant with a value of P ≤ 0.05 were described. A total of 410 samples of domestic poultry were tested, and the results showed 66 (16.1%) viral diseases. The following conditions were the most commonly found diseases in this study: Marek’s disease (42.4%), Infectious bursal disease (31.8%), Avian leukosis (16.6%), Avian pox (7.5%) and Avian infectious bronchitis (1.5%). In this article we discuss the most frequent viral diseases: Marek’s disease (DM) and Gumboro disease. It was also possible to conclude that birds with Marek's disease presented higher odds of developing nerve, tegumentary and locomotors signs (P ≤ 0.05). As well as, more likely to present tumoriform lesions in the liver, spleen, kidneys and heart P ≤ 0.05, as well as lesions in the proventriculus, muscle lesions and in the sciatic nerve P ≤ 0.05. Laying poultry, backyard poultry production and young poultry constitute risk factors for DM (P ≤ 0.05). The results showed that samples with mycotoxicoses were at higher risk of developing DM (P ≤ 0.05). Poultry from industrial system had a higher risk of developing Gumboro disease (P ≤ 0.05). Besides that, domestic poultry with Gumboro disease were more likely to develop signs of depression, nerve signals, tegumentary changes, bursitis, hepatomegaly, renal lesions and splenomegaly (P ≤ 0.05).Discussion: In this research we can observe the domestic poultry with DM presenting tumoriform lesions in several organs, this finding is common in this disease, because it is one of the most frequent viral diseases of neoplastic characteristic. The results show that young birds are a risk factor for DM. In fact, previous works already affirm young birds as being susceptible to disease in relation to adult birds. In this study, laying hens presented a risk factor for DM, however other authors also observed in cut birds or in both types of production. Industrial poultry are at higher risk for DM, this was also reported by other authors, because the disease is of high contagion and morbidity, industrial birds are under high density which facilitates the contact and dissemination of the virus among sick and susceptible birds. In this study, industrial poultry were a risk factor for Gumboro disease, however, other studies show that there is no difference between types of breeding and can also occur in backyard birds. Young poultry and laying hens are more susceptible, and the clinical signs found in the study are compatible with those described in the literature. Through this research on viral diseases, we conclude that our study may complement the already existing official data, since, in a research conducted in the same period, it did not identify the presence of viral diseases in the Southern of RS, but with our research it was possible to register this disease.

Prospective nonscoliotic cohort evaluation of the effects of various positions for obtaining standing lateral thoracolumbar radiographs. The purpose of this study was to compare the effects of various upper extremity positions on thoracolumbar sagittal spinal alignment. The standing position used to capture a lateral plane radiograph can have marked effects on measurements of sagittal spinal alignment and may compound the variability between measurements from successive radiographs. Twenty-two healthy female adolescents performed 3 repeated trials of 4 standing positions in a motion analysis laboratory. The positions included: (1) relaxed standing with arms at sides (CONTROL), (2) standing with fists overlying ipsilateral clavicles (CLAVICLE), (3) active shoulder flexion to 30 degrees with elbows extended (30 ACTIVE), and (4) passive shoulder flexion to 30 degrees with hand supports (30 PASSIVE). Sagittal alignment of the spine was described by kyphosis, lordosis, and the sagittal vertical axis (SVA), all of which were measured from the positions of reflective markers attached to the surface of the back and pelvis. Differences between alignment measures obtained for each of the 3 radiographic positions relative to the functional position (CONTROL) were calculated. Mean differences were then compared between positions using repeated measures ANOVAs (alpha = 0.05). Relative to the CONTROL position, all other positions resulted in negative shifts in SVA (range = -1.1 cm- -4.6 cm), decreased kyphosis (range = -1 degrees- -3 degrees), and increased lordosis (4 degrees for all positions). The shift in the SVA with the 30 PASSIVE position was significantly less than the other 2 positions (P < 0.05) and demonstrated the least variability. Standing with the hands supported while flexing the shoulders 30 degrees during positioning for lateral spinopelvic radiographic acquisition resulted in an SVA and measures of sagittal plane curvature that were comparable with a functional standing position with arms at the side. This seems to be the best way to move the arms anterior to the spine with the least effect on overall sagittal balance.

During development, melanocytes undergo a series of cellular and metabolic changes in order to reach a terminally differentiated state. Some of these changes include extensive proliferation, formation of dendritic projections from the cell membrane, and melanin synthesis. Each of these processes is likely to create distinct metabolic needs, however, little work has been done to investigate how the differentiating melanocyte meets these requirements. Previous work in our lab identified that stromal mesenchymal adipocytes are donors of lipids that aids in melanoma progression and metastasis. This raises the question of whether fatty acid metabolism might also play a role in melanocyte development. Additionally, it indicates a need for further investigation into the role of cellular metabolism in both melanocyte differentiation and related diseases. Herein, we performed a chemical screen as a discovery approach using the Sigma Aldrich LOPAC library containing 1280 compounds in T5D zebrafish embryos and validate the primary hits via titration at different developmental time points. Treatment was initiated at 24‐hour post fertilization for a 48‐hour period at a 10 μM concentration. With this screen we identified a total of fifty compounds which affected melanocyte growth, development, migration pattern, and/or phenotypic appearance in the context of a complete developing organism allowing a more comprehensive analysis. Preliminary data suggest that different pathways such as T‐Cell Receptor inhibition, nitric oxide donors and/or sequesters, inhibition of inflammatory responses from macrophages, basophils and mast cells can potentially affect melanocyte development. Together, these findings help us to better understand the details of melanocyte development and further characterization of these pathways will greatly enhance our understanding of the metabolic requirements of these processes and could yield insight into the role of these processes in melanoma and other disease contexts.