Greedy Scheduling in Multihop Wireless Networks

We study the performance of greedy scheduling in multihop wireless networks where the objective is aggregate utility maximization. Following standard approaches, we consider the dual of the original optimization problem. Optimal scheduling requires selecting independent sets of maximum aggregate price, but this problem is known to be NP-hard. We propose and evaluate a simple greedy heuristic. Analytical bounds on performance are provided and simulations indicate that the greedy heuristic performs well in practice.

Posters versus lurkers. Improving participation in enterprise social networks through management interventions

This thesis investigated the phenomenon of underutilised Enterprise social networks (ESNs). Guided by established theories, we identified key reasons that drive ESN members to either post (i.e., create content) or lurk (i.e., read others' content) and examined the influence of three management interventions - aim to boost participation - on lurkers' and posters' beliefs and participation. We test our model with data collected from 366 members in Google⁺ communities in a large Australian retail organization. We find that posters and lurkers are motivated and hindered by different factors. Moreover, management interventions do not – always – yield the hoped-for results among lurkers.

Designing Dense Radar Networks for Weather Surveillance in Tropical Regions.

Design considerations are presented for a dense weather radar network to support multiple services including aviation. Conflicts, tradeoffs and optimization issues in the context of operation in a tropical region are brought out. The upcoming Indian radar network is used as a case study. Algorithms for data mosaicing are briefly outlined.

Novel 3-dimensional sixfold interpenetrating diamondoid networks of copper(I) coordination polymers of polypyridyl ligands - Syntheses,characterization and crystal structures

Two new coordination polymers [Cu(L-1)(2)](n)(ClO4)(n)center dot 2nH(2)O (1), [Cu(L-2)(2)](n)(ClO4)(n)center dot 2nH(2)O (2) of polydentate imine/pyridyl ligands, L-1 and L-2 with Cu(I) ion have been synthesized and characterized by single crystal X-ray diffraction studies, elemental analyses, IR' UV-vis and NMR spectroscopy. They represent 3-dimensional, sixfold interpenetrating diamondoid network structures having large pores of dimension, 35 x 21 angstrom(2) in 1 and 38 x 19 angstrom(2) in 2, respectively.

Cellular and molecular regulation of mammalian blastocyst hatching

In mammals including humans, failure in blastocyst hatching and implantation leads to early embryonic loss and infertility. Prior to implantation, the blastocyst must hatch out of its acellular glycoprotein coat, the zona pellucida (ZP). The phenomenon of blastocyst hatching is believed to be regulated by (i) dynamic cellular components such as actin-based trophectodermal projections (TEPs), and (ii) a variety of autocrine and paracrine molecules such as growth factors, cytokines and proteases. The spatio-temporal regulation of zona lysis by blastocyst-derived cellular and molecular signaling factors is being keenly investigated. Our studies show that hamster blastocyst hatching is acelerated by growth factors such as heparin binding-epidermal growth factor and leukemia inhibitory factor and that embryo-derived, cysteine proteases including cathepsins are responsible for blastocyst hatching. Additionally, we believe that cyclooxygenase-generated prostaglandins, estradiol-17 beta mediated estrogen receptor-alpha signaling and possibly NF kappa B could be involved in peri-hatching development. Moreover, we show that TEPs are intimately involved with lysing ZP and that the TEPs potentially enrich and harbor hatching-enabling factors. These observations provide new insights into our understanding of the key cellular and molecular regulators involved in the phenomenon of mammalian blastocyst hatching, which is essential for the establishment of early pregnancy.

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.

Downregulation of microRNA-498 in colorectal cancers and its cellular effects

miR-498 is a non-coding RNA located intergenically in 19q13.41. Due to its predicted targeting of several genes involved in control of cellular growth, we examined the expression of miR-498 in colon cancer cell lines and a large cohort of patients with colorectal adenocarcinoma. Two colon cancer cancer cell lines (SW480 and SW48) and one normal colonic epithelial cell line (FHC) were recruited. The expression of miR-498 was tested in these cell lines by using quantitative real-time polymerase chain reaction (qRT-PCR). Tissues from 80 patients with surgical resection of colorectum (60 adenocarcinomas and 20 non-neoplastic tissues) were tested for miR-498 expression by qRT-PCR. In addition, an exogenous miR-498 (mimic) was used to detect the miRNA׳s effects on cell proliferation and cell cycle events in SW480 using MTT calorimetric assay and flow cytometry respectively. The colon cancer cell lines showed reduced expression of miR-498 compared to a normal colonic epithelial cell line. Mimic driven over expression of miR-498 in the SW480 cell line resulted in reduced cell proliferation and increased proportions of G2-M phase cells. In tissues, miR-498 expression was too low to be detected in all colorectal adenocarcinoma compared to non-neoplastic tissues. This suggests that the down regulation of miR-498 in colorectal cancer tissues and the direct suppressive cellular effect noted in cancer cell lines implies that miR-498 has some direct or indirect role in the pathogenesis of colorectal adenocarcinomas.

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.

Pathological changes at the target tissue level in Sjögren's syndrome and their effect on the exocrine function of the salivary glands

Sjögren s syndrome (SS) is a common autoimmune disease affecting the lacrimal and salivary glands. SS is characterized by a considerable female predominance and a late age of onset, commonly at the time of adreno- and menopause. The levels of the androgen prohormone dehydroepiandrosterone-sulphate (DHEA-S) in the serum are lower in patients with SS than in age- and sex-matched healthy control subjects. The eventual systemic effects of low androgen levels in SS are not currently well understood. Basement membranes (BM) are specialized layers of extracellular matrix and are composed of laminin (LM) and type IV collagen matrix networks. BMs deliver messages to epithelial cells via cellular LM-receptors including integrins (Int) and Lutheran blood group antigen (Lu). The composition of BMs and distribution of LM-receptors in labial salivary glands (LSGs) of normal healthy controls and patients with SS was assessed. LMs have complex and highly regulated distribution in LSGs. LMs seem to have specific tasks in the dynamic regulation of acinar cell function. LM-111 is important for the normal acinar cell differentiation and its expression is diminished in SS. Also LM-211 and -411 seem to have some acinar specific functional tasks in LSGs. LM-311, -332 and -511 seem to have more general structure maintaining and supporting roles in LSGs and are relatively intact also in SS. Ints α3β1, α6β1, α6β4 and Lu seem to supply structural basis for the firm attachment of epithelial cells to the BM in LSGs. The expression of Ints α1β1 and α2β1 differed clearly from other LM-receptors in that they were found almost exclusively around the acini and intercalated duct cells in salivons suggesting some type of acinar cell compartment-specific or dominant function. Expression of these integrins was lower in SS compared to healthy controls suggesting that the LM-111 and -211-to-Int α1β1 and α2β1 interactions are defective in SS and are crucial to the maintenance of the acini in LSGs. DHEA/DHEA-S concentration in serum and locally in saliva of patients with SS seems to have effects on the salivary glands. These effects were first detected using the androgen-dependent CRISP-3 protein, the production and secretion of which were clearly diminished in SS. This might be due to the impaired function of the intracrine DHEA prohormone metabolizing machinery, which fails to successfully convert DHEA into its active metabolites in LSGs. The progenitor epithelial cells from the intercalated ductal area of LSGs migrate to the acinar compartment and then undergo a phenotype change into secretory acinar cells. This migration and phenotype change seem to be regulated by the LM-111-to-Int α1β1/Int α2β1 interactions. Lack of these interactions could be one factor limiting the normal remodelling process. Androgens are effective stimulators of Int α1β1 and α2β1 expression in physiologic concentrations. Addition of DHEA to the culture medium had effective stimulating effect on the Int α1β1 and α2β1 expression and its effect may be deficient in the LSGs of patients with SS.

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.