Identification of functional information subgraphs in complex networks

We present a general information theoretic approach for identifying functional subgraphs in complex networks where the dynamics of each node are observable. We show that the uncertainty in the state of each node can be expressed as a sum of information quantities involving a growing number of correlated variables at other nodes. We demonstrate that each term in this sum is generated by successively conditioning mutual informations on new measured variables, in a way analogous to a discrete differential calculus. The analogy to a Taylor series suggests efficient search algorithms for determining the state of a target variable in terms of functional groups of other degrees of freedom. We apply this methodology to electrophysiological recordings of networks of cortical neurons grown it in vitro. Despite strong stochasticity, we show that each cell's patterns of firing are generally explained by the activity of a small number of other neurons. We identify these neuronal subgraphs in terms of their mutually redundant or synergetic character and reconstruct neuronal circuits that account for the state of each target cell.Comment: 4 pages, 4 figure

A novel antibody-based biosensor method for the rapid measurement of PAH contamination in oysters

Conventional PAH analytical methods are time-consuming and expensive, limiting their utility in time sensitive events (i.e. oil spills and floods) or for widespread environmental monitoring. Unreliable and inefficient screening methods intended to prioritize samples for more extensive analyses exacerbate the issue. Antibody-based biosensor technology was implemented as a quantitative screening method to measure total PAH concentration in adult oysters (Crassostrea virginica) — a well-known bioindicator species with ecological and commercial significance. Individual oysters were analyzed throughout the historically polluted Elizabeth River watershed (Virginia, USA). Significant positive association was observed between biosensor and GC–MS measurements that persisted when the method was calibrated for different regulatory subsets of PAHs. Mapping of PAH concentrations in oysters throughout the watershed demonstrates the utility of this technology for environmental monitoring. Through a novel extension of equilibrium partitioning, biosensor technology shows promise as a cost-effective analysis to rapidly predict whole animal exposure to better assess human health risk as well as improve monitoring efforts

Use of Annual Phosphorus Loss Estimator (APLE) Model to Evaluate a Phosphorus Index

The Phosphorus (P) Index was developed to provide a relative ranking of agricultural fields according to their potential for P loss to surface water. Recent efforts have focused on updating and evaluating P Indices against measured or modeled P loss data to ensure agreement in magnitude and direction. Following a recently published method, we modified the Maryland P Site Index (MD-PSI) from a multiplicative to a component index structure and evaluated the MD-PSI outputs against P loss data estimated by the Annual P Loss Estimator (APLE) model, a validated, field-scale, annual P loss model. We created a theoretical dataset of fields to represent Maryland conditions and scenarios and created an empirical dataset of soil samples and management characteristics from across the state. Through the evaluation process, we modified a number of variables within the MD-PSI and calculated weighting coefficients for each P loss component. We have demonstrated that our methods can be used to modify a P Index and increase correlation between P Index output and modeled P loss data. The methods presented here can be easily applied in other states where there is motivation to update an existing P Index

Phosphorylation-dependent translocation of sphingosine kinase to the plasma membrane drives its oncogenic signalling

Sphingosine kinase (SK) 1 catalyzes the formation of the bioactive lipid sphingosine 1-phosphate, and has been implicated in several biological processes in mammalian cells, including enhanced proliferation, inhibition of apoptosis, and oncogenesis. Human SK (hSK) 1 possesses high instrinsic catalytic activity which can be further increased by a diverse array of cellular agonists. We have shown previously that this activation occurs as a direct consequence of extracellular signal–regulated kinase 1/2–mediated phosphorylation at Ser225, which not only increases catalytic activity, but is also necessary for agonist-induced translocation of hSK1 to the plasma membrane. In this study, we report that the oncogenic effects of overexpressed hSK1 are blocked by mutation of the phosphorylation site despite the phosphorylation-deficient form of the enzyme retaining full instrinsic catalytic activity. This indicates that oncogenic signaling by hSK1 relies on a phosphorylation-dependent function beyond increasing enzyme activity. We demonstrate, through constitutive localization of the phosphorylation-deficient form of hSK1 to the plasma membrane, that hSK1 translocation is the key effect of phosphorylation in oncogenic signaling by this enzyme. Thus, phosphorylation of hSK1 is essential for oncogenic signaling, and is brought about through phosphorylation-induced translocation of hSK1 to the plasma membrane, rather than from enhanced catalytic activity of this enzyme

Density-dependence of functional development in spiking cortical networks grown in vitro

During development, the mammalian brain differentiates into specialized regions with distinct functional abilities. While many factors contribute to functional specialization, we explore the effect of neuronal density on the development of neuronal interactions in vitro. Two types of cortical networks, dense and sparse, with 50,000 and 12,000 total cells respectively, are studied. Activation graphs that represent pairwise neuronal interactions are constructed using a competitive first response model. These graphs reveal that, during development in vitro, dense networks form activation connections earlier than sparse networks. Link entropy analysis of dense net- work activation graphs suggests that the majority of connections between electrodes are reciprocal in nature. Information theoretic measures reveal that early functional information interactions (among 3 cells) are synergetic in both dense and sparse networks. However, during later stages of development, previously synergetic relationships become primarily redundant in dense, but not in sparse networks. Large link entropy values in the activation graph are related to the domination of redundant ensembles in late stages of development in dense networks. Results demonstrate differences between dense and sparse networks in terms of informational groups, pairwise relationships, and activation graphs. These differences suggest that variations in cell density may result in different functional specialization of nervous system tissue in vivo.Comment: 10 pages, 7 figure