Robustness and dissipation of mitogen-activated protein kinases signal transduction network: Underlying funneled landscape against stochastic fluctuations

We uncovered the underlying energy landscape of the mitogen-activated protein kinases signal transduction cellular network by exploring the statistical natures of the Brownian dynamical trajectories. We introduce a dimensionless quantity: The robustness ratio of energy gap versus local roughness to measure the global topography of the underlying landscape. A high robustness ratio implies funneled landscape. The landscape is quite robust against environmental fluctuations and variants of the intrinsic chemical reaction rates.

Funneled landscape leads to robustness of cellular networks: MAPK signal transduction

We uncover the underlying potential energy landscape for a cellular network. We find that the potential energy landscape of the mitogen-activated protein-kinase signal transduction network is funneled toward the global minimum. The funneled landscape is quite robust against random perturbations. This naturally explains robustness from a physical point of view. The ratio of slope versus roughness of the landscape becomes a quantitative measure of robustness of the network. Funneled landscape is a realization of the Darwinian principle of natural selection at the cellular network level. It provides an optimal criterion for network connections and design. Our approach is general and can be applied to other cellular networks.

Determination of ethenzamide and acetoaminophen in quaternary powdered samples by near infrared spectroscopy and searching combination moving window partial least squares algorithm: effects of spectral resolution and signal-to-noise ratio

The present study reports an application of the searching combination moving window partial least squares (SCMWPLS) algorithm to the determination of ethenzamide and acetoaminophen in quaternary powdered samples by near infrared (NIR) spectroscopy. Another purpose of the study was to examine the instrumentation effects of spectral resolution and signal-to-noise ratio of the Buchi NIRLab N-200 FT-NIR spectrometer equipped with an InGaAs detector. The informative spectral intervals of NIR spectra of a series of quaternary powdered mixture samples were first located for ethenzamide and acetoaminophen by use of moving window partial least squares regression (MWPLSR). Then, these located spectral intervals were further optimised by SCMWPLS for subsequent partial least squares (PLS) model development. The improved results are attributed to both the less complex PLS models and to higher accuracy of predicted concentrations of ethenzamide and acetoaminophen in the optimised informative spectral intervals that are featured by NIR bands. At the same time, SCMWPLS is also demonstrated as a viable route for wavelength selection.

Discrete wavelets transform for signal denoising in capillary electrophoresis with electrochemiluminescence detection

Discrete wavelets transform (DWT). was applied to noise on removal capillary electrophoresis-electrochemiluminescence (CE-ECL) electropherograms. Several typical wavelet transforms, including Haar, Daublets, Coiflets, and Symmlets, were evaluated. Four types of determining threshold methods, fixed form threshold, rigorous Stein's unbiased estimate of risk (rigorous SURE), heuristic SURE and minimax, combined with hard and soft thresholding methods were compared. The denoising study on synthetic signals showed that wave Symmlet 4 with a level decomposition of 5 and the thresholding method of heuristic SURE-hard provide the optimum denoising strategy. Using this strategy, the noise on CE-ECL electropherograms could be removed adequately. Compared with the Savitzky-Golay and Fourier transform denoising methods, DWT is an efficient method for noise removal with a better preservation of the shape of peaks.

Metabolic Engineering, metabolite profiling and machine learning to investigate the phloem-mobile signal in systemic acquired resistance in tobacco

Draper, J., Darby, R.M., Beckmann, M., Maddison, A.L., Mondhe, M., Sheldrick, C., Taylor, J., Goodacre, R., and Kell, D.B. (2002) Metabolic Engineering, metabolite profiling and machine learning to investigate the phloem-mobile signal in systemic acquired resistance in tobacco. First International Congress on Plant Metabolomics, Wageningen, The Netherlands

Use of semiconductor optical amplifiers in signal processing applications

We describe a 42.6 Gbit/s all-optical pattern recognition system which uses semiconductor optical amplifiers (SOAs). A circuit with three SOA-based logic gates is used to identify the presence of specific port numbers in an optical packet header.

Digital signal processing and artificial intelligence for the automated classification of food allergy

As a by-product of the ‘information revolution’ which is currently unfolding, lifetimes of man (and indeed computer) hours are being allocated for the automated and intelligent interpretation of data. This is particularly true in medical and clinical settings, where research into machine-assisted diagnosis of physiological conditions gains momentum daily. Of the conditions which have been addressed, however, automated classification of allergy has not been investigated, even though the numbers of allergic persons are rising, and undiagnosed allergies are most likely to elicit fatal consequences. On the basis of the observations of allergists who conduct oral food challenges (OFCs), activity-based analyses of allergy tests were performed. Algorithms were investigated and validated by a pilot study which verified that accelerometer-based inquiry of human movements is particularly well-suited for objective appraisal of activity. However, when these analyses were applied to OFCs, accelerometer-based investigations were found to provide very poor separation between allergic and non-allergic persons, and it was concluded that the avenues explored in this thesis are inadequate for the classification of allergy. Heart rate variability (HRV) analysis is known to provide very significant diagnostic information for many conditions. Owing to this, electrocardiograms (ECGs) were recorded during OFCs for the purpose of assessing the effect that allergy induces on HRV features. It was found that with appropriate analysis, excellent separation between allergic and nonallergic subjects can be obtained. These results were, however, obtained with manual QRS annotations, and these are not a viable methodology for real-time diagnostic applications. Even so, this was the first work which has categorically correlated changes in HRV features to the onset of allergic events, and manual annotations yield undeniable affirmation of this. Fostered by the successful results which were obtained with manual classifications, automatic QRS detection algorithms were investigated to facilitate the fully automated classification of allergy. The results which were obtained by this process are very promising. Most importantly, the work that is presented in this thesis did not obtain any false positive classifications. This is a most desirable result for OFC classification, as it allows complete confidence to be attributed to classifications of allergy. Furthermore, these results could be particularly advantageous in clinical settings, as machine-based classification can detect the onset of allergy which can allow for early termination of OFCs. Consequently, machine-based monitoring of OFCs has in this work been shown to possess the capacity to significantly and safely advance the current state of clinical art of allergy diagnosis

Application of signal detection methods to fisheries management

The abundance of many commercially important fish stocks are declining and this has led to widespread concern on the performance of traditional approach in fisheries management. Quantitative models are used for obtaining estimates of population abundance and the management advice is based on annual harvest levels (TAC), where only a certain amount of catch is allowed from specific fish stocks. However, these models are data intensive and less useful when stocks have limited historical information. This study examined whether empirical stock indicators can be used to manage fisheries. The relationship between indicators and the underlying stock abundance is not direct and hence can be affected by disturbances that may account for both transient and persistent effects. Methods from Statistical Process Control (SPC) theory such as the Cumulative Sum (CUSUM) control charts are useful in classifying these effects and hence they can be used to trigger management response only when a significant impact occurs to the stock biomass. This thesis explores how empirical indicators along with CUSUM can be used for monitoring, assessment and management of fish stocks. I begin my thesis by exploring various age based catch indicators, to identify those which are potentially useful in tracking the state of fish stocks. The sensitivity and response of these indicators towards changes in Spawning Stock Biomass (SSB) showed that indicators based on age groups that are fully selected to the fishing gear or Large Fish Indicators (LFIs) are most useful and robust across the range of scenarios considered. The Decision-Interval (DI-CUSUM) and Self-Starting (SS-CUSUM) forms are the two types of control charts used in this study. In contrast to the DI-CUSUM, the SS-CUSUM can be initiated without specifying a target reference point (‘control mean’) to detect out-of-control (significant impact) situations. The sensitivity and specificity of SS-CUSUM showed that the performances are robust when LFIs are used. Once an out-of-control situation is detected, the next step is to determine how much shift has occurred in the underlying stock biomass. If an estimate of this shift is available, they can be used to update TAC by incorporation into Harvest Control Rules (HCRs). Various methods from Engineering Process Control (EPC) theory were tested to determine which method can measure the shift size in stock biomass with the highest accuracy. Results showed that methods based on Grubb’s harmonic rule gave reliable shift size estimates. The accuracy of these estimates can be improved by monitoring a combined indicator metric of stock-recruitment and LFI because this may account for impacts independent of fishing. The procedure of integrating both SPC and EPC is known as Statistical Process Adjustment (SPA). A HCR based on SPA was designed for DI-CUSUM and the scheme was successful in bringing out-of-control fish stocks back to its in-control state. The HCR was also tested using SS-CUSUM in the context of data poor fish stocks. Results showed that the scheme will be useful for sustaining the initial in-control state of the fish stock until more observations become available for quantitative assessments.

Akt signal transduction dysfunction in the 3xTg-AD mouse model of Alzheimer's disease

Alzheimer’s disease (AD) is an incurable neurodegenerative disorder, accounting for over 60% of all cases of dementia. The primary risk factor for AD is age, however several genetic and environmental factors are also involved. The pathological characteristics of AD include extracellular deposition of the beta-amyloid peptide (Aβ) and intraneuronal accumulation of neurofibrillary tangles (NFTs) made of aggregated paired helical filaments (PHFs) of the hyperphosphorylated tau protein, along with synaptic loss and neuronal death. There are numerous biochemical mechanisms involved in AD pathogenesis, however the reigning hypothesis points to toxic oligomeric Aβ species as the primary causative factor in a cascade of events leading to neuronal stress and dyshomeostasis that initiate abnormal regulation of tau. The insulin and IGF-1 receptors (IR, IGF-1R) are the primary activators of PI3- K/Akt through which they regulate cell growth, development, glucose metabolism, and learning and memory. Work in our lab and others shows increased Akt activity and phosphorylation of its downstream targets in AD brain, along with insulin and insulin-like growth factor-1 signalling (IIS) dysfunction. This is supported by studies of AD models in vivo and in vitro. Our group and others hypothesise that Aβ activates Akt through IIS to initiate a negative feedback mechanism that desensitises neurons to insulin/IGF-1, and sustains activation of Akt. In this study the functions of endogenous Akt, IR, and the insulin receptor substrate (IRS-1) were examined in relationship to Aβ and tau pathology in the 3xTg-AD mouse model, which contains three mutant human transgenes associated with familial AD or dementia. The 3xTg-AD mouse develops Aβ and tau pathology in a spatiotemporal manner that best recapitulates the progression of AD in human brain. Western blotting and immunofluorescent microscopy techniques were utilised in vivo and in vitro, to examine the relationship between IIS, Akt, and AD pathology. I first characterised in detail AD pathology in 3xTg-AD mice, where an age-related accumulation of intraneuronal Aβ and tau was observed in the hippocampal formation, amygdala, and entorhinal cortex, and at late stages (18 months), extracellular amyloid plaques and NFTs, primarily in the subiculum and the CA1 layer of the hippocampal formation. Increased activity of Akt, detected with antibody to phosphoSer473-Akt, was increased in 3xTg-AD mice compared to age-matched non-transgenic mice (non-Tg), and in direct correlation to the accumulation of Aβ and tau in neuronal somatodendritic compartments. Akt phosphorylates tau at residue Ser214 within a highly specific consensus sequence for Akt phosphorylation, and phosphoSer214-tau strongly decreases microtubule (MT) stabilisation by preventing tau-MT binding. PhosphoSer214-tau increased concomitantly with this in the same age-related and region-specific fashion. Polarisation of tau phosphorylation was observed, where PHF-1 (tauSer396/404) and phosphoSer214-tau both appeared early in 3xTg-AD mice in distinct neuronal compartments: PHF-1 in axons, and phosphoSer214-tau in neuronal soma and dendrites. At 18 months, phosphoSer214-tau strongly colocalised with NFTs positive for the PHF- 1 and AT8 (tauSer202/Thr205) phosphoepitopes. IR was decreased with age in 3xTg-AD brain and in comparison to age-matched non-Tg, and this was specific for brain regions containing Aβ, tau, and hyperactive Akt. IRS-1 was similarly decreased, and both proteins showed altered subcellular distribution. Phosphorylation of IRS-1Ser312 is a strong indicator of IIS dysfunction and insulin resistance, and was increased in 3xTg-AD mice with age and in relation to pathology. Of particular note was our observation that abberant IIS and Akt signalling in 3xTg-AD brain related to Aβ and tau pathology on a gross anatomical level, and specifically localised to the brain regions and circuitry of the perforant path. Finally, I conducted a preliminary study of the effects of synthetic Aβ oligomers on embryonic rat hippocampus neuronal cultures to support these results and those in the literature. Taken together, these novel findings provide evidence for IIS and Akt signal transduction dysfunction as the missing link between Aβ and tau pathogenesis, and contribute to the overall understanding of the biochemical mechanisms of AD.

All-optical signal processing using semiconductor optical amplifiers for next generation optical networks

This thesis details an experimental and simulation investigation of some novel all-optical signal processing techniques for future optical communication networks. These all-optical techniques include modulation format conversion, phase discrimination and clock recovery. The methods detailed in this thesis use the nonlinearities associated with semiconductor optical amplifiers (SOA) to manipulate signals in the optical domain. Chapter 1 provides an introduction into the work detailed in this thesis, discusses the increased demand for capacity in today’s optical fibre networks and finally explains why all-optical signal processing may be of interest for future optical networks. Chapter 2 discusses the relevant background information required to fully understand the all-optical techniques demonstrated in this thesis. Chapter 3 details some pump-probe measurement techniques used to calculate the gain and phase recovery times of a long SOA. A remarkably fast gain recovery is observed and the wavelength dependent nature of this recovery is investigated. Chapter 4 discusses the experimental demonstration of an all-optical modulation conversion technique which can convert on-off- keyed data into either duobinary or alternative mark inversion. In Chapter 5 a novel phase sensitive frequency conversion scheme capable of extracting the two orthogonal components of a quadrature phase modulated signal into two separate frequencies is demonstrated. Chapter 6 investigates a novel all-optical clock recovery technique for phase modulated optical orthogonal frequency division multiplexing superchannels and finally Chapter 7 provides a brief conclusion.

Temporal variations of IEEE 802.11b signal strengths in an in-building environment

Recent popularity of the IEEE 802.11b Wireless Local Area Networks (WLANs) in a host of current-day applications has instigated a suite of research challenges. The 802.11b WLANs are highly reliable and wide spread. In this work, we study the temporal characteristics of RSSI in the real-working environment by conducting a controlled set of experiments. Our results indicate that a significant variability in the RSSI can occur over time. Some of this variability in the RSSI may be due to systematic causes while the other component can be expressed as stochastic noise. We present an analysis of both these aspects of RSSI. We treat the moving average of the RSSI as the systematic causes and the noise as the stochastic causes. We give a reasonable estimate for the moving average to compute the noise accurately. We attribute the changes in the environment such as the movement of people and the noise associated with the NIC circuitry and the network access point as causes for this variability. We find that the results of our analysis are of primary importance to active research areas such as location determination of users in a WLAN. The techniques used in some of the RF-based WLAN location determination systems, exploit the characteristics of the RSSI presented in this work to infer the location of a wireless client in a WLAN. Thus our results form the building blocks for other users of the exact characteristics of the RSSI.

BOLD signal compartmentalization based on the apparent diffusion coefficient.

Functional MRI (fMRI) can detect blood oxygenation level dependent (BOLD) hemodynamic responses secondary to neuronal activity. The most commonly used method for detecting fMRI signals is the gradient-echo echo-planar imaging (EPI) technique because of its sensitivity and speed. However, it is generally believed that a significant portion of these signals arises from large veins, with additional contribution from the capillaries and parenchyma. Early experiments using diffusion-weighted gradient-echo EPI have suggested that intra-voxel incoherent motion (IVIM) weighting inherent in the sequence can selectively attenuate contributions from different vessels based on the differences in the mobility of the blood within them. In the present study, we used similar approach to characterize the apparent diffusion coefficient (ADC) distribution within the activated areas of BOLD contrast. It is shown that the voxel values of the ADCs obtained from this technique can infer various vascular contributions to the BOLD signal.

Smoothened signal transduction is promoted by G protein-coupled receptor kinase 2.

Deregulation of the Sonic hedgehog pathway has been implicated in an increasing number of human cancers. In this pathway, the seven-transmembrane (7TM) signaling protein Smoothened regulates cellular proliferation and differentiation through activation of the transcription factor Gli. The activity of mammalian Smoothened is controlled by three different hedgehog proteins, Indian, Desert, and Sonic hedgehog, through their interaction with the Smoothened inhibitor Patched. However, the mechanisms of signal transduction from Smoothened are poorly understood. We show that a kinase which regulates signaling by many "conventional" 7TM G-protein-coupled receptors, G protein-coupled receptor kinase 2 (GRK2), participates in Smoothened signaling. Expression of GRK2, but not catalytically inactive GRK2, synergizes with active Smoothened to mediate Gli-dependent transcription. Moreover, knockdown of endogenous GRK2 by short hairpin RNA (shRNA) significantly reduces signaling in response to the Smoothened agonist SAG and also inhibits signaling induced by an oncogenic Smoothened mutant, Smo M2. We find that GRK2 promotes the association between active Smoothened and beta-arrestin 2. Indeed, Gli-dependent signaling, mediated by coexpression of Smoothened and GRK2, is diminished by beta-arrestin 2 knockdown with shRNA. Together, these data suggest that GRK2 plays a positive role in Smoothened signaling, at least in part, through the promotion of an association between beta-arrestin 2 and Smoothened.