Evolution of ecological specialization and venom of a predatory marine gastropod

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74591/1/j.1365-294X.2007.03627.x.pd

Network Synthesis

Contains research objectives and reports on three research projects

Ecological Release and Venom Evolution of a Predatory Marine Snail at Easter Island

BACKGROUND:Ecological release is coupled with adaptive radiation and ecological diversification yet little is known about the molecular basis of phenotypic changes associated with this phenomenon. The venomous, predatory marine gastropod Conus miliaris has undergone ecological release and exhibits increased dietary breadth at Easter Island. METHODOLOGY/PRINCIPAL FINDINGS:We examined the extent of genetic differentiation of two genes expressed in the venom of C. miliaris among samples from Easter Island, American Samoa and Guam. The population from Easter Island exhibits unique frequencies of alleles that encode distinct peptides at both loci. Levels of divergence at these loci exceed observed levels of divergence observed at a mitochondrial gene region at Easter Island. CONCLUSIONS/SIGNIFICANCE:Patterns of genetic variation at two genes expressed in the venom of this C. miliaris suggest that selection has operated at these genes and contributed to the divergence of venom composition at Easter Island. These results show that ecological release is associated with strong selection pressures that promote the evolution of new phenotypes

Can the evolution of music be analyzed in a quantitative manner?

We propose a methodology to study music development by applying multivariate statistics on composers characteristics. Seven representative composers were considered in terms of eight main musical features. Grades were assigned to each characteristic and their correlations were analyzed. A bootstrap method was applied to simulate hundreds of artificial composers influenced by the seven representatives chosen. Afterwards we quantify non-numeric relations like dialectics, opposition and innovation. Composers differences on style and technique were represented as geometrical distances in the feature space, making it possible to quantify, for example, how much Bach and Stockhausen differ from other composers or how much Beethoven influenced Brahms. In addition, we compared the results with a prior investigation on philosophy. Opposition, strong on philosophy, was not remarkable on music. Supporting an observation already considered by music theorists, strong influences were identified between composers by the quantification of dialectics, implying inheritance and suggesting a stronger master-disciple evolution when compared to the philosophy analysis.Comment: 8 pages, 6 figures, added references for sections 1 and 4.C, better mathematical description on section 2. New values and interpretation, now considering a bootstrap metho

BASINS/HSPF: Model use, calibration, and validation

ABSTRACT. This article presents recommendations by model developers and the authors about calibration and validation procedures for the Hydrological Simulatio

Variation and evolution of toxin gene expression patterns of six closely related venomous marine snails

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75661/1/j.1365-294X.2008.03804.x.pd

Near-Surface Interface Detection for Coal Mining Applications Using Bispectral Features and GPR

The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coal-clay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range

CSU data set of the FIRE marine stratocumulus IFO, The

August 1988.Includes bibliographical references.This research was funded by NASA under grant NAG 1-554 and by ONR under grant N00014-87-K-0228/P00001