Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.

A flexible tool for integrated planning of active distribution networks

This paper presents a flexible and integrated planning tool for active distribution network to maximise the benefits of having high level s of renewables, customer engagement, and new technology implementations. The tool has two main processing parts: “optimisation” and “forecast”. The “optimization” part is an automated and integrated planning framework to optimize the net present value (NPV) of investment strategy for electric distribution network augmentation over large areas and long planning horizons (e.g. 5 to 20 years) based on a modified particle swarm optimization (MPSO). The “forecast” is a flexible agent-based framework to produce load duration curves (LDCs) of load forecasts for different levels of customer engagement, energy storage controls, and electric vehicles (EVs). In addition, “forecast” connects the existing databases of utility to the proposed tool as well as outputs the load profiles and network plan in Google Earth. This integrated tool enables different divisions within a utility to analyze their programs and options in a single platform using comprehensive information.

Allostery and conformational free energy changes in human tryptophanyl-tRNA synthetase from essential dynamics and structure networks

The interdependence of the concept of allostery and enzymatic catalysis, and they being guided by conformational mobility is gaining increased prominence. However, to gain a molecular level understanding of llostery and hence of enzymatic catalysis, it is of utter importance that the networks of amino acids participating in allostery be deciphered. Our lab has been exploring the methods of network analysis combined with molecular dynamics simulations to understand allostery at molecular level. Earlier we had outlined methods to obtain communication paths and then to map the rigid/flexible regions of proteins through network parameters like the shortest correlated paths, cliques, and communities. In this article, we advance the methodology to estimate the conformational populations in terms of cliques/communities formed by interactions including the side-chains and then to compute the ligand-induced population shift. Finally, we obtain the free-energy landscape of the protein in equilibrium, characterizing the free-energy minima accessed by the protein complexes. We have chosen human tryptophanyl-tRNA synthetase (hTrpRS), a protein esponsible for charging tryptophan to its cognate tRNA during protein biosynthesis for this investigation. This is a multidomain protein exhibiting excellent allosteric communication. Our approach has provided valuable structural as well as functional insights into the protein. The methodology adopted here is highly generalized to illuminate the linkage between protein structure networks and conformational mobility involved in the allosteric mechanism in any protein with known structure.

Cucumber chloroplast trnL(CAA) gene: nucleotide sequence and in vivo expression analysis in etiolated cucumber seedlings treated with benzyladenine and light.

The nucleotide sequence of a 714 bp BamHI-EcoRI fragment of cucumber chloroplast DNA was determined. The fragment contained a gene for tRNA(Leu) together with its flanking regions. The trnL(CAA) gene sequence is about 99% in similarity to broad bean, cauliflower, maize, spinach and tobacco corresponding genes. The relative expression level of the gene was determined by Northern (tRNA) gel blot and Northern (total cellular RNA) slot-blot analyses using the trnL gene probe in 6-day old etiolated cucumber seedlings and the seedlings that had been kept in the dark (dark-grown), treated with benzyladenine (BA) and kept in the dark (BA-treated dark-grown), illuminated (light-grown), and treated with BA and illuminated (BA-treated light-grown), for additional 4, 8 or 12 hr. The trnL transcripts and tRNA(Leu) levels in BA-treated dark-grown seedlings were 5 and 3 times higher, respectively after 4 hr BA treatment, while in the BA treated light-grown seedlings the level of trnL transcripts was only 3 times higher and had no detectable effect on mature tRNA(Leu) when compared to the time-4 hr dark-grown seedlings. However, the level of mature tRNA(Leu) did not show marked changes in the light-grown seedlings, whereas the level of trnL transcripts increases 3 times after 8 hr illumination of dark-grown seedlings. These data indicate that both light and cytokinin can signal changes in plastid tRNA gene expression. The possible regulatory mechanisms for such changes are discussed.

Cucumber chloroplast trnL(CAA) gene: nucleotide sequence and in vivo expression analysis in etiolated cucumber seedlings treated with benzyladenine and light

The nucleotide sequence of a 714 bp BamHI-EcoRI fragment of cucumber chloroplast DNA was determined. The fragment contained a gene for tRNA(Leu) together with its flanking regions. The trnL(CAA) gene sequence is about 99% in similarity to broad bean, cauliflower, maize, spinach and tobacco corresponding genes. The relative expression level of the gene was determined by Northern (tRNA) gel blot and Northern (total cellular RNA) slot-blot analyses using the trnL gene probe in 6-day old etiolated cucumber seedlings and the seedlings that had been kept in the dark (dark-grown), treated with benzyladenine (BA) and kept in the dark (BA-treated dark-grown), illuminated (light-grown), and treated with BA and illuminated (BA- treated light-grown), for additional 4, 8 or 12 hr. The trnL transcripts and tRNA(Leu) levels in BA-treated dark-grown seedlings were 5 and 3 times higher, respectively after 4 hr BA treatment, while in the BA treated light-grown seedlings the level of trnL transcripts was only 3 times higher and had not detectable effect on mature tRNA(Leu) when compared to the time-4 hr dark-grown seedlings. However, the level of mature tRNA(Leu) did not show marked changes in the light-grown seedlings, whereas the level of trnL transcripts increases 3 times after 8 hr illumination of dark-grown seedlings. These date indicate that both light and cytokinin can signal changes in plastid tRNA gene expression. The possible regulatory mechanisms for such changes are discussed.

Functional and regulatory characteristics of eukaryotic type II DNA topoisomerase

DNA topoisomerases are ubiquitous nuclear enzymes that govern the topological interconversions of DNA by transiently breaking/rejoining the phosphodiester backbone of one (type I) or both (type II) strands of the double helix. Consistent with these functions, topoisomerases play key roles in many aspects of DNA metabolism. Type II DNA topoisomerase (topo II) is vital for various nuclear processes, including DNA replication, chromosome segregation, and maintenance of chromosome structure. Topo II expression is regulated at multiple stages, including transcriptional, posttranscriptional, and posttranslational levels, by a multitude of signaling factors. Topo II is also the cellular target for a variety of clinically relevant anti-tumor drugs. Despite significant progress in our understanding of the role of topo II in diverse nuclear processes, several important aspects of topo II function, expression, and regulation are poorly understood. We have focused this review specifically on eukaryotic DNA topoisomerase II, with an emphasis on functional and regulatory characteristics.

Binding and regulatory properties of phosphofructokinase from swine kidney

The influence of fructose 2,6-bisphosphate on the activation of purified swine kidney phosphofructokinase as a function of the concentration of fructose 6P, ATP and citrate was investigated. The purified enzyme was nearly completely inhibited in the presence of 2 mM ATP. The addition of 20 nM fructose 2,6-P2 reversed the inhibition and restored more than 80% of the activity. In the absence of fructose 2,6-P2 the reaction showed a sigmoidal dependence on fructose-6-phosphate. The addition of 10 nM fructose 2,6-bisphosphate decreased the K0.5 for fructose 6-phosphate from 3 mM to 0.4 mM in the presence of 1.5 mM ATP. These results clearly show that fructose 2,6-bisphosphate increases the affinity of the enzyme for fructose 6-phosphate and decreases the inhibitory effect of ATP. The extent of inhibition by citrate was also significantly decreased in the presence of fructose 2,6-phosphate. The influence of various effectors of phosphofructokinase on the binding of ATP and fructose 6-P to the enzyme was examined in gel filtration studies. It was found that kidney phosphofructokinase binds 5.6 moles of fructose 6-P per mole of enzyme, which corresponds to about one site per subunit of tetrameric enzyme. The KD for fructose 6-P was 13 microM and in the presence of 0.5 mM ATP it increased to 27 microM. The addition of 0.3 mM citrate also increased the KD for fructose 6-P to about 40 microM. AMP, 10 microM, decreased the KD to 5 microM and the addition of fructose 2,6-phosphate decreased the KD for fructose 6-P to 0.9 microM. The addition of these compounds did not effect the maximal amount of fructose 6-P bound to the enzyme, which indicated that the binding site for these compounds might be near, but was not identical to the fructose 6-P binding site. The enzyme bound a maximum of about 12.5 moles of ATP per mole, which corresponds to 3 moles per subunit. The KD of the site with the highest affinity for ATP was 4 microM, and it increased to 15 microM in the presence of fructose 2,6-bisphosphate. The addition of 50 microM fructose 1,6-bisphosphate increased the KD for ATP to 5.9 microM. AMP increased the KD to 5.9 microM whereas 0.3 mM citrate decreased the KD for ATP to about 2 microM.(ABSTRACT TRUNCATED AT 400 WORDS).

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

Analyzing information flow in brain networks with nonparametric Granger causality

Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate both pairwise and conditional measures of Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task.

CsrA interacting small RNAs in Haemophilus spp genomes: a theoretical analysis

The csrA is a carbon storage regulator gene that encodes a protein with multiple RNA interaction sites. Bacterial non-coding small RNAs like csrB, csrC and their counterparts in diverse bacterial genus are identified to control the regulatory activities of CsrA and its orthologs. An attempt has been made in this study to identify 'novel' non-coding small RNAs that are involved in the regulatory activities of csrA gene. All CsrA-interacting small RNAs are computationally fingerprinted to have multiple occurrence of 7-nucleotide CsrA interacting repeats [CAGGA(U/A/C)G] along with a 18-nucleotide upstream binding site. However, in several of the genomes like Haemophilus spp, the upstream binding site is not identified. The current methodology overcomes this difficulty by identifying small RNA-specific orphan transcriptional units within the intergenic regions of the genome. The results could identify all known CsrA-interacting small RNAs in E. coli, Vibrio cholerae and Pseudomonas aeruginosa genomes and additionally has picked six new possible CsrA-interacting small RNA regions in E. coli. Our computational analysis indicates that known rygD and rprA sRNAs in E. coli could possibly interact with CsrA proteins. The study is extended to three of the Haemophilus genomes that could identify seven new possible CsrA interacting small RNAs.

Health complaints and regulatory reform: Implications for vulnerable populations?

Complaints and disciplinary processes play a significant role in health professional regulation. Many countries are transitioning from models of self-regulation to greater external oversight through systems including meta regulation, responsive (risk–based) regulation, and “networked governance”. Such systems harness, in differing ways, public, private, professional and non-governmental bodies to exert influence over the conduct of health professionals and services. Interesting literature is emerging regarding complainants’ motivations and experiences, the impact of complaints processes on health professionals and identification of features such as complainant and health professional profiles, types of complaints and outcomes. This paper concentrates on studies identifying vulnerable groups and their participation in health care regulatory systems.

Correlation, Coding, and Cooperation in Wireless Sensor Networks

We consider a single-hop data-gathering sensor network, consisting of a set of sensor nodes that transmit data periodically to a base-station. We are interested in maximizing the lifetime of this network. With our definition of network lifetime and the assumption that the radio transmission energy consumption forms the most significant portion of the total energy consumption at a sensor node, we attempt to enhance the network lifetime by reducing the transmission energy budget of sensor nodes by exploiting three system-level opportunities. We pose the problem of maximizing lifetime as a max-min optimization problem subject to the constraint of successful data collection and limited energy supply at each node. This turns out to be an extremely difficult optimization to solve. To reduce the complexity of this problem, we allow the sensor nodes and the base-station to interactively communicate with each other and employ instantaneous decoding at the base-station. The chief contribution of the paper is to show that the computational complexity of our problem is determined by the complex interplay of various system-level opportunities and challenges.

Deep Packet Inspection Using Message Passing Networks

We propose a solution based on message passing bipartite networks, for deep packet inspection, which addresses both speed and memory issues, which are limiting factors in current solutions. We report on a preliminary implementation and propose a parallel architecture.