Cats about town: is predation by free-ranging pet cats Felis catus likely to affect urban bird populations?

Even though they are fed daily by their owners, free-ranging pet cats Felis catus may kill wild birds and, given their high densities (typically > 200 cats/km(2)), it has been postulated that cat predation could be a significant negative factor affecting the dynamics of urban bird populations. In this study, we: (1) used questionnaire surveys in 10 sites within the city of Bristol, UK, to estimate cat density; (2) estimated the number of birds killed annually in five sites by asking cat owners to record prey animals returned home; and then (3) compared the number of birds killed with breeding density and productivity to estimate the potential impact of cat predation. In addition, we (4) compared the condition of those birds killed by cats versus those killed in collisions, e.g. window strikes. Mean (+/- sd) cat density was 348 +/- 86 cats/km(2) (n = 10 sites); considering the eight species most commonly taken by cats, the mean ratios of adult birds/cats and juvenile birds/cats across the five sites were 1.17 +/- 0.23 and 3.07 +/- 0.74, respectively. Approximately 60% of the cats studied for up to 1 year at each site never returned any prey home; despite this, the estimated number of birds killed was large relative to their breeding density and productivity in many sites. Across species, cat-killed birds were in significantly poorer condition than those killed following collisions; this is consistent with the notion that cat predation represents a compensatory rather than additive form of mortality. Interpretation of these results is, however, complicated by patterns of body mass regulation in passerines. The predation rates estimated in this study would suggest that cats were likely to have been a major cause of mortality for some species of birds. The effect of cat predation in urban landscapes therefore warrants further investigation. The potential limitations of the current study are discussed, along with suggestions for resolving them.

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.

Song development in birds: the role of early experience and its potential effect on rehabilitation success

Environmental conditions during the early life stages of birds can have significant effects on the quality of sexual signals in adulthood, especially song, and these ultimately have consequences for breeding success and fitness. This has wide-ranging implications for the rehabilitation protocols undertaken in wildlife hospitals which aim to return captive-reared animals to their natural habitat. Here we review the current literature on bird song development and learning in order to determine the potential impact that the rearing of juvenile songbirds in captivity can have on rehabilitation success. We quantify the effects of reduced learning on song structure and relate this to the possible effects on an individual's ability to defend a territory or attract a mate. We show the importance of providing a conspecific auditory model for birds to learn from in the early stages post-fledging, either via live- or tape-tutoring and provide suggestions for tutoring regimes. We also highlight the historical focus on learning in a few model species that has left an information gap in our knowledge for most species reared at wildlife hospitals.

Local roles of TGF-beta superfamily members in the control of ovarian follicle development

Members of the transforming growth factor-beta (TGF-beta) superfamily have wide-ranging influences on many tissue and organ systems including the ovary. Two recently discovered TGF-beta superfamily members, growth/differentiation factor-9 (GDF-9) and bone morphogenetic protein-15 (BMP-15; also designated as GDF-9B) are expressed in an oocyte-specific manner from a very early stage and play a key role in promoting follicle growth beyond the primary stage. Follicle growth to the small antral stage does not require gonadotrophins but appears to be driven by local autocrine/paracrine signals from both somatic cell types (granulosa and theca) and from the oocyte. TGF-beta superfamily members expressed by follicular cells and implicated in this phase of follicle development include TGF-beta, activin, GDF-9/9B and several BMPs. Acquisition of follicle-stimulating hormone (FSH) responsiveness is a pre-requisite for growth beyond the small antral stage and evidence indicates an autocrine role for granulosa-derived activin in promoting granulosa cell proliferation, FSH receptor expression and aromatase activity. Indeed, some of the effects of FSH on granulosa cells may be mediated by endogenous activin. At the same time, activin may act on theca cells to attenuate luteinizing hormone (LH)-dependent androgen production in small to medium-size antral follicles. Dominant follicle selection appears to depend on differential FSH sensitivity amongst a growing cohort of small antral follicles. Activin may contribute to this selection process by sensitizing those follicles with the highest "activin tone" to FSH. Production of inhibin, like oestradiol, increases in selected dominant follicles, in an FSH- and insulin-like growth factor-dependent manner and may exert a paracrine action on theca cells to upregulate LH-induced secretion of androgen, an essential requirement for further oestradiol secretion by the pre-ovulatory follicle. Like activin, BMP-4 and -7 (mostly from theca), and BMP-6 (mostly from oocyte), can enhance oestradiol and inhibin secretion by bovine granulosa cells while suppressing progesterone secretion; this suggests a functional role in delaying follicle luteinization and/or atresia. Follistatin, on the other hand, may favor luteinization and/or atresia by bio-neutralizing intrafollicular activin and BMPs. Activin receptors are expressed by the oocyte and activin may have a further intrafollicular role in the terminal stages of follicle differentiation to promote oocyte maturation and developmental competence. In a reciprocal manner, oocyte-derived GDF-9/9B may act on the surrounding cumulus granulosa cells to attenuate oestradiol output and promote progesterone and hyaluronic acid production, mucification and cumulus expansion.(C) 2003 Elsevier Science B.V. All rights reserved.

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.