Exposure assessment of radon in the drinking water supplies: a descriptive study in Palestine

<p>Abstract</p> <p>Background</p> <p>Radon gas is considered as a main risk factor for lung cancer and found naturally in rock, soil, and water. The objective of this study was to determine the radon level in the drinking water sources in Nablus city in order to set up a sound policy on water management in Palestine.</p> <p>Methods</p> <p>This was a descriptive study carried out in two phases with a random sampling technique in the second phase. Primarily, samples were taken from 4 wells and 5 springs that supplied Nablus city residents. For each source, 3 samples were taken and each was analyzed in 4 cycles by RAD 7 device manufactured by Durridge Company. Secondly, from the seven regions of the Nablus city, three samples were taken from the residential tap water of each region. Regarding the old city, ten samples were taken. Finally, the mean radon concentration value for each source was calculated.</p> <p>Results</p> <p>The mean (range) concentration of radon in the main sources were 6.9 (1.5-23.4) Becquerel/liter (Bq/L). Separately, springs and wells' means were 4.6 Bq/L and 9.5 Bq/L; respectively. For the residential tap water in the 7 regions, the results of the mean (range) concentration values were found to be 1.0 (0.9-1.3) Bq/L. For the old city, the mean (range) concentration values were 2.3 (0.9-3.9) Bq/L.</p> <p>Conclusions</p> <p>Except for Al-Badan well, radon concentrations in the wells and springs were below the United State Environmental Protection Agency maximum contaminated level (U.S EPA MCL). The level was much lower for tap water. Although the concentration of radon in the tap water of old city were below the MCL, it was higher than other regions in the city. Preventive measures and population awareness on radon's exposure are recommended.</p

Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect coregulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1 was not uploaded but is available by contacting the author. 27 pages, 5 figures, 15 supplementary file

Principal-Oscillation-Pattern Analysis of Gene Expression

Principal-oscillation-pattern (POP) analysis is a multivariate and systematic technique for identifying the dynamic characteristics of a system from time-series data. In this study, we demonstrate the first application of POP analysis to genome-wide time-series gene-expression data. We use POP analysis to infer oscillation patterns in gene expression. Typically, a genomic system matrix cannot be directly estimated because the number of genes is usually much larger than the number of time points in a genomic study. Thus, we first identify the POPs of the eigen-genomic system that consists of the first few significant eigengenes obtained by singular value decomposition. By using the linear relationship between eigengenes and genes, we then infer the POPs of the genes. Both simulation data and real-world data are used in this study to demonstrate the applicability of POP analysis to genomic data. We show that POP analysis not only compares favorably with experiments and existing computational methods, but that it also provides complementary information relative to other approaches

Understanding network concepts in modules

<p>Abstract</p> <p>Background</p> <p>Network concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory.</p> <p>Results</p> <p>Here we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node 'conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in module networks.</p> <p>Conclusion</p> <p>Using protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from the following webpage: <url>http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworks</url></p

Simultaneous Clustering of Multiple Gene Expression and Physical Interaction Datasets

Many genome-wide datasets are routinely generated to study different aspects of biological systems, but integrating them to obtain a coherent view of the underlying biology remains a challenge. We propose simultaneous clustering of multiple networks as a framework to integrate large-scale datasets on the interactions among and activities of cellular components. Specifically, we develop an algorithm JointCluster that finds sets of genes that cluster well in multiple networks of interest, such as coexpression networks summarizing correlations among the expression profiles of genes and physical networks describing protein-protein and protein-DNA interactions among genes or gene-products. Our algorithm provides an efficient solution to a well-defined problem of jointly clustering networks, using techniques that permit certain theoretical guarantees on the quality of the detected clustering relative to the optimal clustering. These guarantees coupled with an effective scaling heuristic and the flexibility to handle multiple heterogeneous networks make our method JointCluster an advance over earlier approaches. Simulation results showed JointCluster to be more robust than alternate methods in recovering clusters implanted in networks with high false positive rates. In systematic evaluation of JointCluster and some earlier approaches for combined analysis of the yeast physical network and two gene expression datasets under glucose and ethanol growth conditions, JointCluster discovers clusters that are more consistently enriched for various reference classes capturing different aspects of yeast biology or yield better coverage of the analysed genes. These robust clusters, which are supported across multiple genomic datasets and diverse reference classes, agree with known biology of yeast under these growth conditions, elucidate the genetic control of coordinated transcription, and enable functional predictions for a number of uncharacterized genes

Prenatal Cocaine Exposure Increases Synaptic Localization of a Neuronal RasGEF, GRASP-1 via Hyperphosphorylation of AMPAR Anchoring Protein, GRIP

Prenatal cocaine exposure causes sustained phosphorylation of the synaptic anchoring protein, glutamate receptor interacting protein (GRIP1/2), preventing synaptic targeting of the GluR2/3-containing alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid-type glutamate receptors (AMPARs; J. Neurosci. 29: 6308–6319, 2009). Because overexpression of GRIP-associated neuronal rasGEF protein (GRASP-1) specifically reduces the synaptic targeting of AMPARs, we hypothesized that prenatal cocaine exposure enhances GRASP-1 synaptic membrane localization leading to hyper-activation of ras family proteins and heightened actin polymerization. Our results show a markedly increased GRIP1-associated GRASP-1 content with approximately 40% reduction in its rasGEF activity in frontal cortices (FCX) of 21-day-old (P21) prenatal cocaine-exposed rats. This cocaine effect is the result of a persistent protein kinase C (PKC)- and downstream Src tyrosine kinase-mediated GRIP phosphorylation. The hyperactivated PKC also increased membrane-associated GRASP-1 and activated small G-proteins RhoA, cdc42/Rac1 and Rap1 as well as filamentous actin (F-actin) levels without an effect on the phosphorylation state of actin. Since increased F-actin facilitates protein transport, our results suggest that increased GRASP-1 synaptic localization in prenatal cocaine-exposed brains is an adaptive response to restoring the synaptic expression of AMPA-GluR2/3. Our earlier data demonstrated that persistent PKC-mediated GRIP phosphorylation reduces GluR2/3 synaptic targeting in prenatal cocaine-exposed brains, we now show that the increased GRIP-associated GRASP-1 may contribute to the reduction in GluR2/3 synaptic expression and AMPAR signaling defects

How hybrids manage growth and social–business tensions in global supply chains: the case of impact sourcing

This study contributes to the growing interest in how hybrid organizations manage paradoxical social–business tensions. Our empirical case is ‘‘impact sourcing’’— hybrids in global supply chains that hire staff from disadvantaged communities to provide services to business clients. We identify two major growth orientations— ‘‘community-focused’’ and ‘‘client-focused’’ growth—their inherent tensions and ways that hybrids manage them. The former favors slow growth and manages tensions through highly integrated client and community relations; the latter promotes faster growth and manages client and community relations separately. Both growth orientations address social–business tensions in particular ways, but also create latent constraints that manifest when entrepreneurial aspirations conflict with the current growth path. In presenting and discussing our findings, we introduce preempting management practices of tensions, and the importance of geographic embeddedness and distance to the paradox literature