25,895 research outputs found

    Children at risk : their phonemic awareness development in holistic instruction

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    Includes bibliographical references (p. 17-19

    The omega-3 fatty acid, eicosapentaenoic acid (EPA), prevents the damaging effects of tumour necrosis factor (TNF)-alpha during murine skeletal muscle cell differentiation

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    Background: Eicosapentaenoic acid (EPA) is a -3 polyunsaturated fatty acid with antiinflammatory and anti-cachetic properties that may have potential benefits with regards to skeletal muscle atrophy conditions where inflammation is present. It is also reported that pathologic levels of the pro-inflammatory cytokine tumour necrosis factor (TNF)-α are associated with muscle wasting, exerted through inhibition of myogenic differentiation and enhanced apoptosis. These findings led us to hypothesize that EPA may have a protective effect against skeletal muscle damage induced by the actions of TNF-α. Results: The deleterious effects of TNF-α on C2C12 myogenesis were completely inhibited by co-treatment with EPA. Thus, EPA prevented the TNF-mediated loss of MyHC expression and significantly increased myogenic fusion (p < 0.05) and myotube diameter (p < 0.05) indices back to control levels. EPA protective activity was associated with blocking cell death pathways as EPA completely attenuated TNF-mediated increases in caspase-8 activity (p < 0.05) and cellular necrosis (p < 0.05) back to their respective control levels. EPA alone significantly reduced spontaneous apoptosis and necrosis of differentiating myotubes (p < 0.001 and p < 0.05, respectively). A 2 hour pre-treatment with EPA, prior to treatment with TNF alone, gave similar results. Conclusion: In conclusion, EPA has a protective action against the damaging effects of TNF-α on C2C12 myogenesis. These findings support further investigations of EPA as a potential therapeutic agent during skeletal muscle regeneration following injury

    The effect of background knowledge on young children's comprehension of explicit and implicit information

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    Bibliography: leaves 15-16Supported in part by the National Institute of Educatio

    Extracting quantum dynamics from genetic learning algorithms through principal control analysis

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    Genetic learning algorithms are widely used to control ultrafast optical pulse shapes for photo-induced quantum control of atoms and molecules. An unresolved issue is how to use the solutions found by these algorithms to learn about the system's quantum dynamics. We propose a simple method based on covariance analysis of the control space, which can reveal the degrees of freedom in the effective control Hamiltonian. We have applied this technique to stimulated Raman scattering in liquid methanol. A simple model of two-mode stimulated Raman scattering is consistent with the results.Comment: 4 pages, 5 figures. Presented at coherent control Ringberg conference 200

    Adaptive cancelation of self-generated sensory signals in a whisking robot

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    Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme

    AN EMPIRICAL INVESTIGATION INTO DECISION SUPPORT ENVIRONMENTS: FINDINGS AND CONSIDERATIONS

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    The environment in which a DSS is developed can have a significant impact on the development and satisfaction provided by the DSS. A questionnaire was sent to nonacademic TIMS members in an attemmpt to identify specific DSS environments, the capabilities provided by these DSS, and environmental factors that significantly influenced the DSS environment. This paper presents the results of this investigation

    Control of Raman Lasing in the Nonimpulsive Regime

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    We explore coherent control of stimulated Raman scattering in the nonimpulsive regime. Optical pulse shaping of the coherent pump field leads to control over the stimulated Raman output. A model of the control mechanism is investigated.Comment: 4 pages, 5 figure

    Principal Component Analysis with Noisy and/or Missing Data

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    We present a method for performing Principal Component Analysis (PCA) on noisy datasets with missing values. Estimates of the measurement error are used to weight the input data such that compared to classic PCA, the resulting eigenvectors are more sensitive to the true underlying signal variations rather than being pulled by heteroskedastic measurement noise. Missing data is simply the limiting case of weight=0. The underlying algorithm is a noise weighted Expectation Maximization (EM) PCA, which has additional benefits of implementation speed and flexibility for smoothing eigenvectors to reduce the noise contribution. We present applications of this method on simulated data and QSO spectra from the Sloan Digital Sky Survey.Comment: Accepted for publication in PASP; v2 with minor updates, mostly to bibliograph

    De-blending Deep Herschel Surveys: A Multi-wavelength Approach

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    Cosmological surveys in the far infrared are known to suffer from confusion. The Bayesian de-blending tool, XID+, currently provides one of the best ways to de-confuse deep Herschel SPIRE images, using a flat flux density prior. This work is to demonstrate that existing multi-wavelength data sets can be exploited to improve XID+ by providing an informed prior, resulting in more accurate and precise extracted flux densities. Photometric data for galaxies in the COSMOS field were used to constrain spectral energy distributions (SEDs) using the fitting tool CIGALE. These SEDs were used to create Gaussian prior estimates in the SPIRE bands for XID+. The multi-wavelength photometry and the extracted SPIRE flux densities were run through CIGALE again to allow us to compare the performance of the two priors. Inferred ALMA flux densities (Fi^i), at 870μ\mum and 1250μ\mum, from the best fitting SEDs from the second CIGALE run were compared with measured ALMA flux densities (Fm^m) as an independent performance validation. Similar validations were conducted with the SED modelling and fitting tool MAGPHYS and modified black body functions to test for model dependency. We demonstrate a clear improvement in agreement between the flux densities extracted with XID+ and existing data at other wavelengths when using the new informed Gaussian prior over the original uninformed prior. The residuals between Fm^m and Fi^i were calculated. For the Gaussian prior, these residuals, expressed as a multiple of the ALMA error (σ\sigma), have a smaller standard deviation, 7.95σ\sigma for the Gaussian prior compared to 12.21σ\sigma for the flat prior, reduced mean, 1.83σ\sigma compared to 3.44σ\sigma, and have reduced skew to positive values, 7.97 compared to 11.50. These results were determined to not be significantly model dependent. This results in statistically more reliable SPIRE flux densities.Comment: 8 pages, 7 figures, 3 tables. Accepted for publication in A&
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