Optimal division of data for neural network models in water resources applications

The way that available data are divided into training, testing, and validation subsets can have a significant influence on the performance of an artificial neural network (ANN). Despite numerous studies, no systematic approach has been developed for the optimal division of data for ANN models. This paper presents two methodologies for dividing data into representative subsets, namely, a genetic algorithm (GA) and a self-organizing map (SOM). These two methods are compared with the conventional approach commonly used in the literature, which involves an arbitrary division of the data. A case study is presented in which ANN models developed using each data division technique are used to forecast salinity in the River Murray at Murray Bridge (South Australia) 14 days in advance. When tested on a validation data set from July 1992 to March 1998, the models developed using the GA and SOM data division techniques resulted in a reduction in RMS error of 24.2% and 9.9%, respectively, over the conventional data division method. It was found that a SOM could be used to diagnose why an ANN model has performed poorly, given that the poor performance is primarily related to the data themselves and not the choice of the ANN's parameters or architecture.Gavin J. Bowden, Holger R. Maier and Graeme C. Dand

The integrated analysis of metabolic and protein interaction networks reveals novel molecular organizing principles

Background: The study of biological interaction networks is a central theme of systems biology. Here, we investigate the relationships between two distinct types of interaction networks: the metabolic pathway map and the protein-protein interaction network (PIN). It has long been established that successive enzymatic steps are often catalyzed by physically interacting proteins forming permanent or transient multi-enzymes complexes. Inspecting high-throughput PIN data, it was shown recently that, indeed, enzymes involved in successive reactions are generally more likely to interact than other protein pairs. In our study, we expanded this line of research to include comparisons of the underlying respective network topologies as well as to investigate whether the spatial organization of enzyme interactions correlates with metabolic efficiency. Results: Analyzing yeast data, we detected long-range correlations between shortest paths between proteins in both network types suggesting a mutual correspondence of both network architectures. We discovered that the organizing principles of physical interactions between metabolic enzymes differ from the general PIN of all proteins. While physical interactions between proteins are generally dissortative, enzyme interactions were observed to be assortative. Thus, enzymes frequently interact with other enzymes of similar rather than different degree. Enzymes carrying high flux loads are more likely to physically interact than enzymes with lower metabolic throughput. In particular, enzymes associated with catabolic pathways as well as enzymes involved in the biosynthesis of complex molecules were found to exhibit high degrees of physical clustering. Single proteins were identified that connect major components of the cellular metabolism and may thus be essential for the structural integrity of several biosynthetic systems. Conclusion: Our results reveal topological equivalences between the protein interaction network and the metabolic pathway network. Evolved protein interactions may contribute significantly towards increasing the efficiency of metabolic processes by permitting higher metabolic fluxes. Thus, our results shed further light on the unifying principles shaping the evolution of both the functional (metabolic) as well as the physical interaction network

Forecasting water resources variables using artificial neural networks / by Gavin James Bowden.

"February 2003."Corrigenda for, inserted at backIncludes bibliographical references (leaves 475-524 )xxx, 524 leaves : ill. ; 30 cm.A methodology is formulated for the successful design and implementation of artificial neural networks (ANN) models for water resources applications. Attention is paid to each of the steps that should be followed in order to develop an optimal ANN model; including when ANNs should be used in preference to more conventional statistical models; dividing the available data into subsets for modelling purposes; deciding on a suitable data transformation; determination of significant model inputs; choice of network type and architecture; selection of an appropriate performance measure; training (optimisation) of the networks weights; and, deployment of the optimised ANN model in an operational environment. The developed methodology is successfully applied to two water resorces case studies; the forecasting of salinity in the River Murray at Murray Bridge, South Australia; and the the forecasting of cyanobacteria (Anabaena spp.) in the River Murray at Morgan, South Australia.Thesis (Ph.D.)--University of Adelaide, School of Civil and Environmental Engineering, 200

Giant magnetic modulation of a planar, hybrid metamolecule resonance

Coupling magnetic elements to metamaterial structures creates hybrid metamolecules with new opportunities. Here we report on the magnetic control of a metamolecule resonance, by utilizing the interaction between a single split ring resonator (SRR) and a magnetic thin film of permalloy. To suppress eddy current shielding, the permalloy films are patterned into arrays of 30–500 µm diameter discs. Strong hybridized resonances were observed at the anticrossing between the split ring resonance and the ferromagnetic resonance (FMR) of the permalloy. In particular, it is possible to achieve 40 dB modulation of the electric (symmetric) mode of the SRR on sweeping the applied magnetic field through the SRR/FMR anticrossing. The results open the way to the design of planar metamaterials, with potential applications in nonlinear metamaterials, tunable metamaterials and spintronics

Magnetic control of a meta-molecule

Metamaterials offer the prospect of new science and applications. They have been designed by shaping or changing the material of the individual meta-molecules to achieve properties not naturally attainable. Composite meta-molecules incorporating a magnetic component offer new opportunities. In this work we report on the interaction between a non-magnetic split ring resonator (SRR) and a thin film of yttrium iron garnet (YIG). Strong hybridized resonances are observed. While the SRR is characterized by a magnetic and electric resonance, in practice, it is found that the YIG couples strongly to this symmetric (electric) mode of the SRR. It is also demonstrated that the anti-crossing region provides fertile ground for the creation of elementary excitations such as backward volume magnetostatic waves

Neurogenic plasma leakage in mouse airways

1. This study sought to determine whether neurogenic inflammation occurs in the airways by examining the effects of capsaicin or substance P on microvascular plasma leakage in the trachea and lungs of male pathogen-free C57BL/6 mice. 2. Single bolus intravenous injections of capsaicin (0.5 and 1 μmol kg(−1), i.v.) or substance P (1, 10 and 37 nmol kg(−1), i.v.) failed to induce significant leakage in the trachea, assessed as extravasation of Evans blue dye, but did induce leakage in the urinary bladder and skin. 3. Pretreatment with captopril (2.5 mg kg(−1), i.v.), a selective inhibitor of angiotensin converting enzyme (ACE), either alone or in combination with phosphoramidon (2.5 mg kg(−1), i.v.), a selective inhibitor of neutral endopeptidase (NEP), increased baseline leakage of Evans blue in the absence of any exogenous inflammatory mediator. The increase was reversed by the bradykinin B(2) receptor antagonist Hoe 140 (0.1 mg kg(−1), i.v.). 4. After pretreatment with phosphoramidon and captopril, capsaicin increased the Evans blue leakage above the baseline in the trachea, but not in the lung. This increase was reversed by the tachykinin (NK(1)) receptor antagonist SR 140333 (0.7 mg kg(−1), i.v.), but not by the NK(2) receptor antagonist SR 48968 (1 mg kg(−1), i.v.). 5. Experiments using Monastral blue pigment as a tracer localized the leakage to postcapillary venules in the trachea and intrapulmonary bronchi, although the labelled vessels were less numerous in mice than in comparably treated rats. Blood vessels of the pulmonary circulation were not labelled. 6. We conclude that neurogenic inflammation can occur in airways of pathogen-free mice, but only after the inhibition of enzymes that normally degrade inflammatory peptides. Neurogenic inflammation does not involve the pulmonary microvasculature