Modeling Austrian Consumer Responses to a Vignette Television Commercial Drama For a Vacation Resort Destination

Abstract- This project involved the formulation and test of a model of Austrian consumers\u27 cognitive and affective responses to a vignette television commercial drama for a vacation resort area, the Halkidiki region of Greece. Results indicate that response to the commercial was both cognitive and affective with sympathy and empathy, mediating the influence of verisimilitude on attitudes toward the ad and brand. These results were consistent with what was expected from a sample of consumers from a low power distance and moderate individualistic culture such as Austria. Results suggest that to promote tourism services effectively, a commercial\u27s production value and realism must be high to produce verisimilitude and, in turn, sufficient sympathy and empathy to influence attitudes

SITE-SPECIFIC VERSUS WHOLE-FIELD FERTILITY AND LIME MANAGEMENT IN MICHIGAN SOYBEANS AND CORN

Prior research into variable-rate application (VRA) of fertilizer nutrients has found profitability to be lacking in single nutrient applications to U.S. cereal crops. This study examines the yield and cost effects of VRA phosphorus, potassium and lime application on Michigan corn and soybean farm fields in 1998-2001. After four years, we found no yield gain from site-specific management, but statistically significant added costs, resulting in no gain in profitability. Contrary to results elsewhere, there was no evidence of enhanced spatial yield stability due to site-specific fertility management. Likewise, there was no evidence of decreased variability of phosphorus, potassium or lime after VRA treatment. Site-specific response functions and yield goals might also enhance the likelihood of profitable VRA in the future.Crop Production/Industries,

Localization and function of Kinesin-5-like proteins during assembly and maintenance of mitotic spindles in Silvetia compressa

<p>Abstract</p> <p>Background</p> <p>Kinesin-5 (Eg-5) motor proteins are essential for maintenance of spindle bipolarity in animals. The roles of Kinesin-5 proteins in other systems, such as Arabidopsis, Dictyostelium, and sea urchin are more varied. We are studying Kinesin-5-like proteins during early development in the brown alga <it>Silvetia compressa</it>. Previously, this motor was shown to be needed to assemble a bipolar spindle, similar to animals. This report builds on those findings by investigating the localization of the motor and probing its function in spindle maintenance.</p> <p>Findings</p> <p>Anti-Eg5 antibodies were used to investigate localization of Kinesin-5-like proteins in brown algal zygotes. In interphase zygotes, localization was predominantly within the nucleus. As zygotes entered mitosis, these motor proteins strongly associated with spindle poles and, to a lesser degree, with the polar microtubule arrays and the spindle midzone. In order to address whether Kinesin-5-like proteins are required to maintain spindle bipolarity, we applied monastrol to synchronized zygotes containing bipolar spindles. Monastrol is a cell-permeable chemical inhibitor of the Kinesin-5 class of molecular motors. We found that inhibition of motor function in pre-formed spindles induced the formation of multipolar spindles and short bipolar spindles.</p> <p>Conclusion</p> <p>Based upon these localization and inhibitor studies, we conclude that Kinesin-5-like motors in brown algae are more similar to the motors of animals than those of plants or protists. However, Kinesin-5-like proteins in <it>S. compressa </it>serve novel roles in spindle formation and maintenance not observed in animals.</p

Do Neural Factors Underlie Age Differences in Rapid Ankle Torque Development?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111232/1/j.1532-5415.1996.tb03737.x.pd

Fractal light from lasers

Fractals, complex shapes with structure at multiple scales, have long been observed in Nature: as symmetric fractals in plants and sea shells, and as statistical fractals in clouds, mountains and coastlines. With their highly polished spherical mirrors, laser resonators are almost the precise opposite of Nature, and so it came as a surprise when, in 1998, transverse intensity cross-sections of the eigenmodes of unstable canonical resonators were predicted to be fractals [Karman et al., Nature 402, 138 (1999)]. Experimental verification has so far remained elusive. Here we observe a variety of fractal shapes in transverse intensity cross-sections through the lowest-loss eigenmodes of unstable canonical laser resonators, thereby demonstrating the controlled generation of fractal light inside a laser cavity. We also advance the existing theory of fractal laser modes, first by predicting 3D self-similar fractal structure around the centre of the magnified self-conjugate plane, second by showing, quantitatively, that intensity cross-sections are most self-similar in the magnified self-conjugate plane. Our work offers a significant advance in the understanding of a fundamental symmetry of Nature as found in lasers

Gaussian mixture models and machine learning predict megakaryocytic growth and differentiation potential ex vivo

The ability to analyze single cells via flow cytometry has resulted in a wide range of biological and medical applications. Currently, there is no established framework to compare and interpret time-series flow cytometry data for cell engineering applications. Manual analysis of temporal trends is time-consuming and subjective for large-scale datasets. We resolved this bottleneck by developing TEmporal Gaussian Mixture models (TEGM), an unbiased computational strategy to quantify and predict temporal trends of developing cell subpopulations indicative of cellular phenotype. TEGM applies Gaussian mixture models and gradient boosted trees for cell engineering applications. TEGM enables the extraction of subtle features, such as the dispersion and rate of change of surface marker expression for each subpopulation over time. These critical, yet hard-to-discern, features are fed into machine-learning algorithms that predict underlying cell classes. Our framework can be flexibly applied to conventional flow cytometry sampling schemes, and allows for faster and more consistent processing of time-series flow cytometry data. Please click Additional Files below to see the full abstract

Using Gaussian mixture models and machine learning to predict donor- dependent megakaryocytic cell growth and differentiation potential ex vivo

The ability to analyze single cells via flow cytometry has resulted in a wide range of biological and medical applications. Currently, there is no established framework to compare and interpret time-series flow cytometry data for cell engineering applications. Manual analysis of temporal trends is time-consuming and subjective for large-scale datasets. We resolved this bottleneck by developing TEmporal Gaussian Mixture models (TEGM), an unbiased computational strategy to quantify and predict temporal trends of developing cell subpopulations indicative of cellular phenotype.. Please click Additional Files below to see the full abstract

Soliton Dynamics in Computational Anatomy

Computational anatomy (CA) has introduced the idea of anatomical structures being transformed by geodesic deformations on groups of diffeomorphisms. Among these geometric structures, landmarks and image outlines in CA are shown to be singular solutions of a partial differential equation that is called the geodesic EPDiff equation. A recently discovered momentum map for singular solutions of EPDiff yields their canonical Hamiltonian formulation, which in turn provides a complete parameterization of the landmarks by their canonical positions and momenta. The momentum map provides an isomorphism between landmarks (and outlines) for images and singular soliton solutions of the EPDiff equation. This isomorphism suggests a new dynamical paradigm for CA, as well as new data representation.Comment: published in NeuroImag

Identification of dynamic myoelectric signal-to-force models during isometric lumbar muscle contractions

A 14-muscle myoelectric signal (MES)-driven muscle force prediction model of the L3-L4 cross section is developed which includes a dynamic MES-force relationship and allows for cocontraction. Model parameters are estimated from MES and moments data recorded during rapid exertions in trunk flexion, extension, lateral bending and axial twist. Nine young healthy males participated in the experimental testing. The model used in the parameter estimation is of the output error type. Consistent and physically feasible parameter estimates were obtained by normalizing the RMS MES to maximum exertion levels and using nonlinear constrained optimization to minimize a cost function consisting of the trace of the output error covariance matrix. Model performance was evaluated by comparing measured and MES-predicted moments over a series of slow and rapid exertions. Moment prediction errors were on the order of 25, 30 and 40% during attempted trunk flexion-extensions, lateral bends and axial twists, respectively. The model and parameter estimation methods developed provide a means to estimate lumbar muscle and spine loads, as well as to empirically investigate the use and effects of cocontraction during physical task performances.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31486/1/0000408.pd