Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

Pericellular fibronectin is required for RhoA-dependent responses to cyclic strain in fibroblasts

To test the hypothesis that the pericellular fibronectin matrix is involved in mechanotransduction, we compared the response of normal and fibronectin-deficient mouse fibroblasts to cyclic substrate strain. Normal fibroblasts seeded on vitronectin in fibronectin-depleted medium deposited their own fibronectin matrix. In cultures exposed to cyclic strain, RhoA was activated, actin-stress fibers became more prominent, MAL/MKL1 shuttled to the nucleus, and mRNA encoding tenascin-C was induced. By contrast, these RhoA-dependent responses to cyclic strain were suppressed in fibronectin knockdown or knockout fibroblasts grown under identical conditions. On vitronectin substrate, fibronectin-deficient cells lacked fibrillar adhesions containing alpha5 integrin. However, when fibronectin-deficient fibroblasts were plated on exogenous fibronectin, their defects in adhesions and mechanotransduction were restored. Studies with fragments indicated that both the RGD-synergy site and the adjacent heparin-binding region of fibronectin were required for full activity in mechanotransduction, but not its ability to self-assemble. In contrast to RhoA-mediated responses, activation of Erk1/2 and PKB/Akt by cyclic strain was not affected in fibronectin-deficient cells. Our results indicate that pericellular fibronectin secreted by normal fibroblasts is a necessary component of the strain-sensing machinery. Supporting this hypothesis, induction of cellular tenascin-C by cyclic strain was suppressed by addition of exogenous tenascin-C, which interferes with fibronectin-mediated cell spreading.

Quorum sensing in Escherichia coli and Salmonella typhimurium

Escherichia coli and Salmonella typhimurium strains grown in Luria–Bertani medium containing glucose secrete a small soluble heat labile organic molecule that is involved in intercellular communication. The factor is not produced when the strains are grown in Luria–Bertani medium in the absence of glucose. Maximal secretion of the substance occurs in midexponential phase, and the extracellular activity is degraded as the glucose is depleted from the medium or by the onset of stationary phase. Destruction of the signaling molecule in stationary phase indicates that, in contrast to other quorum-sensing systems, quorum sensing in E. coli and S. typhimurium is critical for regulating behavior in the prestationary phase of growth. Our results further suggest that the signaling factor produced by E. coli and S. typhimurium is used to communicate both the cell density and the metabolic potential of the environment. Several laboratory and clinical strains of E. coli and S. typhimurium were screened for production of the signaling molecule, and most strains make it under conditions similar to those shown here for E. coli AB1157 and S. typhimurium LT2. However, we also show that E. coli strain DH5α does not make the soluble factor, indicating that this highly domesticated strain has lost the gene(s) or biosynthetic machinery necessary to produce the signaling substance. Implications for the involvement of quorum sensing in pathogenesis are discussed.

Modulation of an early step in the secretory machinery in hippocampal nerve terminals

In hippocampal neurons, neurotransmitter release can be regulated by protein kinase A (PKA) through a direct action on the secretory machinery. To identify the site of PKA modulation, we have taken advantage of the ability of the neurotoxin Botulinum A to cleave the synaptic protein SNAP-25. Cleavage of this protein decreases the Ca2+ responsiveness of the secretory machinery by partially uncoupling Ca2+-sensing from fusion per se. This is expressed as a shift toward higher Ca2+ levels of the Ca2+ to neurotransmitter release relationship and as a perturbation of synaptic delay under conditions where secretion induced by the Ca2+-independent secretagogue ruthenium red is unimpaired. We find that SNAP-25 cleavage also perturbs PKA-dependent modulation of secretion; facilitation of ruthenium red-evoked neurotransmitter release by the adenylyl cyclase activator forskolin is blocked completely after Botulinum toxin A action. Together with our observation that forskolin modifies the Ca2+ to neurotransmitter release relationship, our results suggest that SNAP-25 acts as a functional linker between Ca2+ detection and fusion and that PKA modulates an early step in the secretory machinery related to calcium sensing to facilitate synaptic transmission.

The effects of sensing and seizing of market opportunities and reconfiguring activities on the organisational resource base

This study has important implications for marketing theory and practice. In an era of turbulent market environments, the organisational ability to sense and seize market opportunities and to reconfigure the resource base accordingly, has significant effects on performance. This paper uses a dynamic capability framework to explain more explicitly the intricacies of the relationship between sensing and seizing of market opportunities and reconfiguring the resource base (i.e. dynamic capabilities) and the resource base. We investigate how the attributes of dynamic capability deployment, timing, frequency and speed, influence the resource base. We test the proposed framework using survey data from 228 large organisations. Findings show that the timing and frequency of dynamic capability deployment have significant effects on the resource base.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Microstructural characterization of electron beam evaporated tungsten oxide films for gas sensing applications

Tungsten trioxide is one of the potential semiconducting materials used for sensing NH3, CO, CH4 and acetaldehyde gases. The current research aims at development, microstructural characterization and gas sensing properties of thin films of Tungsten trioxide (WO3). In this paper, we intend to present the microstructural characterization of these films as a function of post annealing heat treatment. Microstructural and elemental analysis of electron beam evaporated WO3 thin films and iron doped WO3 films (WO3:Fe) have been carried out using analytical techniques such as Transmission electron microscopy, Rutherford Backscattered Spectroscopy and XPS analysis. TEM analysis revealed that annealing at 300oC for 1 hour improves cyrstallinity of WO3 film. Both WO3 and WO3:Fe films had uniform thickness and the values corresponded to those measured during deposition. RBS results show a fairly high concentration of oxygen at the film surface as well as in the bulk for both films, which might be due to adsorption of oxygen from atmosphere or lattice oxygen vacancy inherent in WO3 structure. XPS results indicate that tungsten exists in 4d electronic state on the surface but at a depth of 10 nm, both 4d and 4f electronic states were observed. Atomic force microscopy reveals nanosize particles and porous structure of the film. This study shows e-beam evaporation technique produces nanoaparticles and porous WO3 films suitable for gas sensing applications and doping with iron decreases the porosity and particle size which can help improve the gas selectivity.

Design and evaluation of an image analysis platform for low-power, low-bandwidth camera networks

We describe the design and evaluation of a platform for networks of cameras in low-bandwidth, low-power sensor networks. In our work to date we have investigated two different DSP hardware/software platforms for undertaking the tasks of compression and object detection and tracking. We compare the relative merits of each of the hardware and software platforms in terms of both performance and energy consumption. Finally we discuss what we believe are the ongoing research questions for image processing in WSNs.

Comparison of high-speed videokeratoscopy and ultrasound distance sensing for measuring the longitudinal corneal apex movements

Two different methods to measure binocular longitudinal corneal apex movements were synchronously applied. High-speed videokeratoscopy at a sampling frequency of 15 Hz and a customdesigned ultrasound distance sensor at 100 Hz were used for the left and the right eye, respectively. Four healthy subjects participated in the study. Simultaneously, cardiac electric cycle (ECG) was registered for each subject at 100 Hz. Each measurement took 20 s. Subjects were asked to suppress blinking during the measurements. A rigid headrest and a bite-bar were used to minimize undesirable head movements. Time, frequency and time-frequency representations of the acquired signals were obtained to establish their temporal and spectral contents. Coherence analysis was used to estimate the correlation between the measured signals. The results showed close correlation between both corneal apex movements and the cardiopulmonary system. Unraveling these relationships could lead to better understanding of interactions between ocular biomechanics and vision. The advantages and disadvantages of the two methods in the context of measuring longitudinal movements of the corneal apex are outlined.

Evaluation of aerial remote sensing techniques for vegetation management in power line corridors

The following paper presents an evaluation of airborne sensors for use in vegetation management in powerline corridors. Three integral stages in the management process are addressed including, the detection of trees, relative positioning with respect to the nearest powerline and vegetation height estimation. Image data, including multi-spectral and high resolution, are analyzed along with LiDAR data captured from fixed wing aircraft. Ground truth data is then used to establish the accuracy and reliability of each sensor thus providing a quantitative comparison of sensor options. Tree detection was achieved through crown delineation using a Pulse-Coupled Neural Network (PCNN) and morphologic reconstruction applied to multi-spectral imagery. Through testing it was shown to achieve a detection rate of 96%, while the accuracy in segmenting groups of trees and single trees correctly was shown to be 75%. Relative positioning using LiDAR achieved a RMSE of 1.4m and 2.1m for cross track distance and along track position respectively, while Direct Georeferencing achieved RMSE of 3.1m in both instances. The estimation of pole and tree heights measured with LiDAR had a RMSE of 0.4m and 0.9m respectively, while Stereo Matching achieved 1.5m and 2.9m. Overall a small number of poles were missed with detection rates of 98% and 95% for LiDAR and Stereo Matching.