Bayesian classification of acoustical waveforms under environmental variability

We present in this paper a new multivariate probabilistic approach to Acoustic Pulse Recognition (APR) for tangible interface applications. This model uses Principle Component Analysis (PCA) in a probabilistic framework to classify tapping pulses with a high degree of variability. It was found that this model, achieves a higher robustness to pulse variability than simpler template matching methods, specifically when allowed to train on data containing high variability. © 2011 IEEE.

Probabilistic computation in human perception under variability in encoding precision.

A key function of the brain is to interpret noisy sensory information. To do so optimally, observers must, in many tasks, take into account knowledge of the precision with which stimuli are encoded. In an orientation change detection task, we find that encoding precision does not only depend on an experimentally controlled reliability parameter (shape), but also exhibits additional variability. In spite of variability in precision, human subjects seem to take into account precision near-optimally on a trial-to-trial and item-to-item basis. Our results offer a new conceptualization of the encoding of sensory information and highlight the brain's remarkable ability to incorporate knowledge of uncertainty during complex perceptual decision-making.

Variability in encoding precision accounts for visual short-term memory limitations.

It is commonly believed that visual short-term memory (VSTM) consists of a fixed number of "slots" in which items can be stored. An alternative theory in which memory resource is a continuous quantity distributed over all items seems to be refuted by the appearance of guessing in human responses. Here, we introduce a model in which resource is not only continuous but also variable across items and trials, causing random fluctuations in encoding precision. We tested this model against previous models using two VSTM paradigms and two feature dimensions. Our model accurately accounts for all aspects of the data, including apparent guessing, and outperforms slot models in formal model comparison. At the neural level, variability in precision might correspond to variability in neural population gain and doubly stochastic stimulus representation. Our results suggest that VSTM resource is continuous and variable rather than discrete and fixed and might explain why subjective experience of VSTM is not all or none.

Morphological variability between wild populations and inbred stocks of a Chinese minnow, Gobiocypris rarus

Gobiocypris rarus, a small, native cyprinid fish, is currently widely used in research on fish pathology, genetics, toxicology, embryology, and physiology in China. To develop this species as a model laboratory animal, inbred strains have been successfully created. In this study, to explore a method to discriminate inbred strains and evaluate inbreeding effects, morphological variation among three wild populations and three inbred stocks of G. rarus was investigated by the multivariate analysis of eight meristic and 30 morphometric characters. Tiny intraspecific variations in meristic characters were found, but these were not effective for population distinction. Stepwise discriminant analysis and cluster analysis of conventional measures and truss network data showed considerabe divergence among populations, especially between wild populations and inbred stocks. The average discriminant accuracy for all populations was 82.1% based on conventional measures and 86.4% based on truss data, whereas the discriminant accuracy for inbred strains was much higher. These results suggested that multivariate analyses of morphometric characters are an effective method for discriminating inbred strains of G. rarus. Morphological differences between wild populations and inbred strains appear to result from both genetic differences and environmental factors. Thirteen characters, extracted from stepwise discriminant analysis, played important roles in morphological differentiation. These characters were mainly measures related to body depth and head size.

High variability of the gvpA-gvpC region in Microcystis

Gas vesicles provide buoyancy to Microcystis and other common cyanobacterial bloom-forming species. gvpA and gvpC are structural genes encoding gas vesicle proteins. Phylogenetic analyses of 10 Microcystis strains/uncultured samples showed that gvpC and each intergenic segment of the gvpA-gvpC region can be divided into two types. The combination of different types of gvpC and intergenic segments is an important factor that diversifies this genomic region. Some Microcystis strains isolated in China possess a 172 to 176 bp sequence tag in the intergenic segment between gvpA and gvpC. The gvpA-gvpC region in Microcystis can be divided into at least 4 classes and more numbers of subclasses. Compared to rbcLX and other regions, the high variability of the gvpA-gvpC region should be more useful in identifying geographical isolates or ecotypes of Microcystis.

Microsatellites assessment of Chinese sturgeon (Acipenser sinensis Gray) genetic variability

Four microsatellites were used to examine the genetic variability of the spawning stocks of Chinese sturgeon, Acipenser sinensis, from the Yangtze River sampled over a 3-year period (1999-2001). Within 60 individuals, a total of 28 alleles were detected over four polymorphic microsatellite loci. The number of alleles per locus ranged from 4 to 15, with an average allele number of 7. The number of genotypes per locus ranged from 6 to 41. The genetic diversity of four microsatellite loci varied from 0.34 to 0.67, with an average value of 0.54. For the four microsatellite loci, the deviation from the Hardy-Weinberg equilibrium was mainly due to null alleles. The mean number of alleles per locus and the mean heterozygosity were lower than the average values known for anadromous fishes. Fish were clustered according to their microsatellite characteristics using an unsupervised 'Artificial Neural Networks' method entitled 'Self-organizing Map'. The results revealed no significant genetic differentiation considering genetic distance among samples collected during different years. Lack of heterogeneity among different annual groups of spawning stocks was explained by the complex age structure (from 8 to 27 years for males and 12 to 35 years for females) of Chinese sturgeon, leading to formulate an hypothesis about the maintenance of genetic diversity and stability in long-lived animals.