Blind source separation for non-stationary mixing

The original publication is available at www.springerlink.comBlind source separation attempts to recover independent sources which have been linearly mixed to produce observations. We consider blind source separation with non-stationary mixing, but stationary sources. The linear mixing of the independent sources is modelled as evolving according to a Markov process, and a method for tracking the mixing and simultaneously inferring the sources is presented. Observational noise is included in the model. The technique may be used for online filtering or retrospective smoothing. The tracking of mixtures of temporally correlated is examined and sampling from within a sliding window is shown to be effective for destroying temporal correlations. The method is illustrated with numerical examples

Inferring the eigenvalues of covariance matrices from limited, noisy data

ArticleCopyright © 2000 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.The eigenvalue spectrum of covariance matrices is of central importance to a number of data analysis techniques, Usually, the sample covariance matrix is constructed from a limited number of noisy samples, We describe a method of inferring the true eigenvalue spectrum from the sample spectrum. Results of Silverstein, which characterize the eigenvalue spectrum of the noise covariance matrix, and inequalities between the eigenvalues of Hermitian matrices are used to infer probability densities for the eigenvalues of the noise-free covariance matrix, using Bayesian inference. Posterior densities for each eigenvalue are obtained, which yield error estimates. The evidence framework gives estimates of the noise variance anal permits model order selection by estimating the rank of the covariance matrix, The method is illustrated with numerical examples

Initial Conditions for Models of Dynamical Systems

The long-time behaviour of many dynamical systems may be effectively predicted by a low-dimensional model that describes the evolution of a reduced set of variables. We consider the question of how to equip such a low-dimensional model with appropriate initial conditions, so that it faithfully reproduces the long-term behaviour of the original high-dimensional dynamical system. Our method involves putting the dynamical system into normal form, which not only generates the low-dimensional model, but also provides the correct initial conditions for the model. We illustrate the method with several examples. Keywords: normal form, isochrons, initialisation, centre manifoldComment: 24 pages in standard LaTeX, 66K, no figure

Muscle Biochemistry and the Ontogeny of Flight Capacity during Behavioral Development in the Honey Bee, Apis Mellifera

A Fundamental Issue in Physiology and Behavior is Understanding the Functional and Genetic Mechanisms that Underlie Major Behavioral Shifts in Organisms as They Adopt New Environments or Life History Tactics. Such Transitions Are Common in Nature and Include the Age-Related Switch from Nest/hive Work to Foraging in Social Insects Such as Honeybees (Apis Mellifera). Because of their Experimental Tractability, Recently Sequenced Genome and Well Understood Biology, Honeybees Are an Ideal Model System for Integrating Molecular, Genetic, Physiological and Sociobiological Perspectives to Advance Understanding of Behavioral and Life History Transitions. When Honeybees (Apis Mellifera) Transition from Hive Work to Foraging, their Flight Muscles Undergo Changes that Allow These Insects to Attain the Highest Rates of Flight Muscle Metabolism and Power Output Ever Recorded in the Animal Kingdom. Here, We Review Research to Date Showing that Honeybee Flight Muscles Undergo Significant Changes in Biochemistry and Gene Expression and that These Changes Accompany a Significant Increase in the Capacity to Generate Metabolic and Aerodynamic Power during Flight. It is Likely that Changes in Muscle Gene Expression, Biochemistry, Metabolism and Functional Capacity May Be Driven Primarily by Behavior as Opposed to Age, as is the Case for Changes in Honeybee Brains

Osteoporosis-pseudoglioma syndrome in South Africa

The osteoporosis-pseudoglioma syndrome (MIM 259770) is a rare autosomal recessive disorder in which bone fragility and frequent fractures are associated with serious ocular changes. The skeletal manifestations resemble those of osteogenesis imperfecta while hyperplasia of the vitreous, eye and corneal opacities often mimics the appearance of intraocular glioma. This disorder was previously reported in a South African family of Indian stock as 'the ocular form of osteogenesis imperfecta'. Terminological discussion followed and it was suggested that these individuals had osteoporosis-pseudoglioma syndrome. This article describes and depicts the manifestations of the disorder and discusses the nosology.DHE

Effects of Flight on Gene Expression and Aging in the Honey Bee Brain and Flight Muscle

Honey bees move through a series of in-hive tasks (e.g., “nursing”) to outside tasks (e.g., “foraging”) that are coincident with physiological changes and higher levels of metabolic activity. Social context can cause worker bees to speed up or slow down this process, and foragers may revert back to their earlier in-hive tasks accompanied by reversion to earlier physiological states. To investigate the effects of flight, behavioral state and age on gene expression, we used whole-genome microarrays and real-time PCR. Brain tissue and flight muscle exhibited different patterns of expression during behavioral transitions, with expression patterns in the brain reflecting both age and behavior, and expression patterns in flight muscle being primarily determined by age. Our data suggest that the transition from behaviors requiring little to no flight (nursing) to those requiring prolonged flight bouts (foraging), rather than the amount of previous flight per se, has a major effect on gene expression. Following behavioral reversion there was a partial reversion in gene expression but some aspects of forager expression patterns, such as those for genes involved in immune function, remained. Combined with our real-time PCR data, these data suggest an epigenetic control and energy balance role in honey bee functional senescence

Changing Fitness Consequences of Hsp70 Copy Number in Transgenic Drosophila Larvae Undergoing Natural Thermal Stress

1. Transgenic Manipulation of the Gene Copy Number of Hsp70, Which Encodes the Major Inducible Heat-Shock Protein of Drosophila Melanogaster (Hsp70), Affects Both Hsp70 Levels and Inducible Thermotolerance in the Laboratory; Here Parallel Effects in Transgenic Drosophila Larvae Undergoing Natural or Simulated Natural Thermal Stress Are Demonstrated. 2. Necrotic Fruit Was Infested with Larvae of Either of Two Transgenic Strains, One Transformed with 12 Extra Copies of the Hsp70 Gene (Extra-Copy Strain) and a Sister Strain Possessing Only the Wild-Type Number (10) of Hsp70 Genes (Excision Strain), and Then Allowed to Heat to Variable Extents. 3. as the Intensity of Thermal Stress Increased, the Consequences of Extra Hsp70 Copies Reversed. after No or Moderate Thermal Stress, Excision Larvae Survived Better Than Did Extra Copy Larvae. by Contrast, Extra Copy Larvae Tolerated Intense Hyperthermia Better Than Did Excision Larvae. 4. These Results Establish that the Hsp70-Mediated Enhancement of Stress Tolerance, Previously Demonstrated Only for Artificial Stress Regimes in the Laboratory, Extends to Natural Stress Regimes. 5. Mortality Due to overexpression of Hsp70, However, Also Increases under Mild Natural Stress Regimes, Buttressing the Ecological Relevance of a Hypothesized Evolutionary Trade-Off of the Benefits and Adverse Consequences of Hsp70 Expression

Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection

Background: Voice disorders affect patients profoundly, and acoustic tools can potentially measure voice function objectively. Disordered sustained vowels exhibit wide-ranging phenomena, from nearly periodic to highly complex, aperiodic vibrations, and increased "breathiness". Modelling and surrogate data studies have shown significant nonlinear and non-Gaussian random properties in these sounds. Nonetheless, existing tools are limited to analysing voices displaying near periodicity, and do not account for this inherent biophysical nonlinearity and non-Gaussian randomness, often using linear signal processing methods insensitive to these properties. They do not directly measure the two main biophysical symptoms of disorder: complex nonlinear aperiodicity, and turbulent, aeroacoustic, non-Gaussian randomness. Often these tools cannot be applied to more severe disordered voices, limiting their clinical usefulness.

Methods: This paper introduces two new tools to speech analysis: recurrence and fractal scaling, which overcome the range limitations of existing tools by addressing directly these two symptoms of disorder, together reproducing a "hoarseness" diagram. A simple bootstrapped classifier then uses these two features to distinguish normal from disordered voices.

Results: On a large database of subjects with a wide variety of voice disorders, these new techniques can distinguish normal from disordered cases, using quadratic discriminant analysis, to overall correct classification performance of 91.8% plus or minus 2.0%. The true positive classification performance is 95.4% plus or minus 3.2%, and the true negative performance is 91.5% plus or minus 2.3% (95% confidence). This is shown to outperform all combinations of the most popular classical tools.

Conclusions: Given the very large number of arbitrary parameters and computational complexity of existing techniques, these new techniques are far simpler and yet achieve clinically useful classification performance using only a basic classification technique. They do so by exploiting the inherent nonlinearity and turbulent randomness in disordered voice signals. They are widely applicable to the whole range of disordered voice phenomena by design. These new measures could therefore be used for a variety of practical clinical purposes.
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