Perception and modification of plant flavonoid signals by rhizosphere microorganisms

Flavonoids are a diverse class of polyphenolic compounds that are produced as a result of plant secondary metabolism. They are known to play a multifunctional role in rhizospheric plant-microbe and plant-plant communication. Most familiar is their function as a signal in initiation of the legume-rhizobia symbiosis, but, flavonoids may also be signals in the establishment of arbuscular mycorrhizal symbiosis and are known agents in plant defence and in allelopathic interactions. Flavonoid perception by, and impact on, their microbial targets (e.g. rhizobia, plant pathogens) is relatively well characterized. However, potential impacts on 'non-target' rhizosphere inhabitants ('non-target' is used to distinguish those microorganisms not conventionally known as targets) have not been thoroughly investigated. Thus, this review first summarizes the conventional roles of flavonoids as nod gene inducers, phytoalexins and allelochemicals before exploring questions concerning 'non-target' impacts. We hypothesize that flavonoids act to shape rhizosphere microbial community structure because they represent a potential source of carbon and toxicity and that they impact on rhizosphere function, for example, by accelerating the biodegradation of xenobiotics. We also examine the reverse question, 'how do rhizosphere microbial communities impact on flavonoid signals?' The presence of microorganisms undoubtedly influences the quality and quantity of flavonoids present in the rhizosphere, both through modification of root exudation patterns and microbial catabolism of exudates. Microbial alteration and attenuation of flavonoid signals may have ecological consequences for below-ground plant-microbe and plant-plant interaction. We have a lack of knowledge concerning the composition, concentration and bioavailability of flavonoids actually experienced by microbes in an intact rhizosphere, but this may be addressed through advances in microspectroscopic and biosensor techniques. Through the use of plant mutants defective in flavonoid biosynthesis, we may also start to address the question of the significance of flavonoids in shaping rhizosphere community structure and function.

Life cycle of the QBO-modulated 11-year solar cycle signals in the Northern Hemispheric winter

This paper provides some insights on the quasi-biennial oscillation (QBO) modulated 11-year solar cycle (11-yr SC) signals in Northern Hemisphere (NH) winter temperature and zonal wind. Daily ERA-40 Reanalysis and ECMWF Operational data for the period of 1958-2006 were used to examine the seasonal evolution of the QBO-solar cycle relationship at various pressure levels up to the stratopause. The results show that the solar signals in the NH winter extratropics are indeed QBO-phase dependent, moving poleward and downward as winter progresses with a faster descent rate under westerly QBO than under easterly QBO. In the stratosphere, the signals are highly significant in late January to early March and have a life span of 30-50 days. Under westerly QBO, the stratospheric solar signals clearly lead and connect to those in the troposphere in late March and early April where they have a life span of 10 days. As the structure changes considerably from the upper stratosphere to the lower troposphere, the exact month when the maximum solar signals occur depends largely on the altitude chosen. For the low-latitude stratosphere, our analysis supports a vertical double-peaked structure of positive signature of the 11-yr SC in temperature, and demonstrates that this structure is further modulated by the QBO. These solar signals have a longer life span (3-4 months) in comparison to those in the extratropics. The solar signals in the lower stratosphere are stronger in early winter but weaker in late winter, while the reverse holds in the upper stratosphere.

Viral nucleolar localisation signals determine dynamic trafficking within the nucleolus

Localisation of both viral and cellular proteins to the nucleolus is determined by a variety of factors including nucleolar localisation signals (NoLSs), but how these signals operate is not clearly understood. The nucleolar trafficking of wild type viral proteins and chimeric proteins, which contain altered NoLSs, were compared to investigate the role of NoLSs in dynamic nucleolar trafficking. Three viral proteins from diverse viruses were selected which localised to the nucleolus; the coronavirus infectious bronchitis virus nucleocapsid (N) protein, the herpesvirus saimiri ORF57 protein and the HIV-1 Rev protein. The chimeric proteins were N protein and ORF57 protein which had their own NoLS replaced with those from ORF57 and Rev proteins, respectively. By analysing the sub-cellular localisation and trafficking of these viral proteins and their chimeras within and between nucleoli using confocal microscopy and photo-bleaching we show that NoLSs are responsible for different nucleolar localisations and trafficking rates.

Design and analysis of climate model experiments for the efficient estimation of anthropogenic signals

Presented herein is an experimental design that allows the effects of several radiative forcing factors on climate to be estimated as precisely as possible from a limited suite of atmosphere-only general circulation model (GCM) integrations. The forcings include the combined effect of observed changes in sea surface temperatures, sea ice extent, stratospheric (volcanic) aerosols, and solar output, plus the individual effects of several anthropogenic forcings. A single linear statistical model is used to estimate the forcing effects, each of which is represented by its global mean radiative forcing. The strong colinearity in time between the various anthropogenic forcings provides a technical problem that is overcome through the design of the experiment. This design uses every combination of anthropogenic forcing rather than having a few highly replicated ensembles, which is more commonly used in climate studies. Not only is this design highly efficient for a given number of integrations, but it also allows the estimation of (nonadditive) interactions between pairs of anthropogenic forcings. The simulated land surface air temperature changes since 1871 have been analyzed. The changes in natural and oceanic forcing, which itself contains some forcing from anthropogenic and natural influences, have the most influence. For the global mean, increasing greenhouse gases and the indirect aerosol effect had the largest anthropogenic effects. It was also found that an interaction between these two anthropogenic effects in the atmosphere-only GCM exists. This interaction is similar in magnitude to the individual effects of changing tropospheric and stratospheric ozone concentrations or to the direct (sulfate) aerosol effect. Various diagnostics are used to evaluate the fit of the statistical model. For the global mean, this shows that the land temperature response is proportional to the global mean radiative forcing, reinforcing the use of radiative forcing as a measure of climate change. The diagnostic tests also show that the linear model was suitable for analyses of land surface air temperature at each GCM grid point. Therefore, the linear model provides precise estimates of the space time signals for all forcing factors under consideration. For simulated 50-hPa temperatures, results show that tropospheric ozone increases have contributed to stratospheric cooling over the twentieth century almost as much as changes in well-mixed greenhouse gases.

A novel spectral representation of electromyographic signals

Time/frequency and temporal analyses have been widely used in biomedical signal processing. These methods represent important characteristics of a signal in both time and frequency domain. In this way, essential features of the signal can be viewed and analysed in order to understand or model the physiological system. Historically, Fourier spectral analyses have provided a general method for examining the global energy/frequency distributions. However, an assumption inherent to these methods is the stationarity of the signal. As a result, Fourier methods are not generally an appropriate approach in the investigation of signals with transient components. This work presents the application of a new signal processing technique, empirical mode decomposition and the Hilbert spectrum, in the analysis of electromyographic signals. The results show that this method may provide not only an increase in the spectral resolution but also an insight into the underlying process of the muscle contraction.

The application of the Hilbert spectrum to the analysis of electromyographic signals

This paper investigates the application of the Hilbert spectrum (HS), which is a recent tool for the analysis of nonlinear and nonstationary time-series, to the study of electromyographic (EMG) signals. The HS allows for the visualization of the energy of signals through a joint time-frequency representation. In this work we illustrate the use of the HS in two distinct applications. The first is for feature extraction from EMG signals. Our results showed that the instantaneous mean frequency (IMNF) estimated from the HS is a relevant feature to clinical practice. We found that the median of the IMNF reduces when the force level of the muscle contraction increases. In the second application we investigated the use of the HS for detection of motor unit action potentials (MUAPs). The detection of MUAPs is a basic step in EMG decomposition tools, which provide relevant information about the neuromuscular system through the morphology and firing time of MUAPs. We compared, visually, how MUAP activity is perceived on the HS with visualizations provided by some traditional (e.g. scalogram, spectrogram, Wigner-Ville) time-frequency distributions. Furthermore, an alternative visualization to the HS, for detection of MUAPs, is proposed and compared to a similar approach based on the continuous wavelet transform (CWT). Our results showed that both the proposed technique and the CWT allowed for a clear visualization of MUAP activity on the time-frequency distributions, whereas results obtained with the HS were the most difficult to interpret as they were extremely affected by spurious energy activity. (c) 2008 Elsevier Inc. All rights reserved.

Subspace system identification framework for the analysis of multimoded propagation of THz-transient signals

We provide a system identification framework for the analysis of THz-transient data. The subspace identification algorithm for both deterministic and stochastic systems is used to model the time-domain responses of structures under broadband excitation. Structures with additional time delays can be modelled within the state-space framework using additional state variables. We compare the numerical stability of the commonly used least-squares ARX models to that of the subspace N4SID algorithm by using examples of fourth-order and eighth-order systems under pulse and chirp excitation conditions. These models correspond to structures having two and four modes simultaneously propagating respectively. We show that chirp excitation combined with the subspace identification algorithm can provide a better identification of the underlying mode dynamics than the ARX model does as the complexity of the system increases. The use of an identified state-space model for mode demixing, upon transformation to a decoupled realization form is illustrated. Applications of state-space models and the N4SID algorithm to THz transient spectroscopy as well as to optical systems are highlighted.

Rejoinder to 'Performance of different synchronisation measures in real data: A case study on electroencephalograhic signals

We agree with Duckrow and Albano [Phys. Rev. E 67, 063901 (2003)] and Quian Quiroga et al. [Phys. Rev. E 67, 063902 (2003)] that mutual information (MI) is a useful measure of dependence for electroencephalogram (EEG) data, but we show that the improvement seen in the performance of MI on extracting dependence trends from EEG is more dependent on the type of MI estimator rather than any embedding technique used. In an independent study we conducted in search for an optimal MI estimator, and in particular for EEG applications, we examined the performance of a number of MI estimators on the data set used by Quian Quiroga et al. in their original study, where the performance of different dependence measures on real data was investigated [Phys. Rev. E 65, 041903 (2002)]. We show that for EEG applications the best performance among the investigated estimators is achieved by k-nearest neighbors, which supports the conjecture by Quian Quiroga et al. in Phys. Rev. E 67, 063902 (2003) that the nearest neighbor estimator is the most precise method for estimating MI.

Comment on "Performance of different synchronization measures in real data: A case study on electroencephalographic signals"

We agree with Duckrow and Albano [Phys. Rev. E 67, 063901 (2003)] and Quian Quiroga [Phys. Rev. E 67, 063902 (2003)] that mutual information (MI) is a useful measure of dependence for electroencephalogram (EEG) data, but we show that the improvement seen in the performance of MI on extracting dependence trends from EEG is more dependent on the type of MI estimator rather than any embedding technique used. In an independent study we conducted in search for an optimal MI estimator, and in particular for EEG applications, we examined the performance of a number of MI estimators on the data set used by Quian Quiroga in their original study, where the performance of different dependence measures on real data was investigated [Phys. Rev. E 65, 041903 (2002)]. We show that for EEG applications the best performance among the investigated estimators is achieved by k-nearest neighbors, which supports the conjecture by Quian Quiroga in Phys. Rev. E 67, 063902 (2003) that the nearest neighbor estimator is the most precise method for estimating MI.