An Examination of the Effects of Type of Sport Participation on Weight Classification and Academic Achievement: Academic Persistence as a Predictor

This study evaluated effectiveness of a soccer intervention for reducing obesity and increasing academic performance in low-income elementary school children by analyzing data regarding sport participation, academic performance, classroom behavior, and BMI using ANOVAS and Chi-Squared tests. While the intervention did not directly affect weight classification or academic performance, the type of sport(s) students participated in did. Team sport players had significantly higher weights and fared the worst academically, followed by those who played individual sports. Non-athletes fared the best. The type of sport a child played influenced academic persistence, which influenced academic performance

Manifestations of Drag Reduction by Polymer Additives in Decaying, Homogeneous, Isotropic Turbulence

The existence of drag reduction by polymer additives, well established for wall-bounded turbulent flows, is controversial in homogeneous, isotropic turbulence. To settle this controversy we carry out a high-resolution direct numerical simulation (DNS) of decaying, homogeneous, isotropic turbulence with polymer additives. Our study reveals clear manifestations of drag-reduction-type phenomena: On the addition of polymers to the turbulent fluid we obtain a reduction in the energy dissipation rate, a significant modification of the fluid energy spectrum especially in the deep-dissipation range, a suppression of small-scale intermittency, and a decrease in small-scale vorticity filaments.Comment: 4 pages, 3 figure

Adiabatic quantum computation along quasienergies

The parametric deformations of quasienergies and eigenvectors of unitary operators are applied to the design of quantum adiabatic algorithms. The conventional, standard adiabatic quantum computation proceeds along eigenenergies of parameter-dependent Hamiltonians. By contrast, discrete adiabatic computation utilizes adiabatic passage along the quasienergies of parameter-dependent unitary operators. For example, such computation can be realized by a concatenation of parameterized quantum circuits, with an adiabatic though inevitably discrete change of the parameter. A design principle of adiabatic passage along quasienergy is recently proposed: Cheon's quasienergy and eigenspace anholonomies on unitary operators is available to realize anholonomic adiabatic algorithms [Tanaka and Miyamoto, Phys. Rev. Lett. 98, 160407 (2007)], which compose a nontrivial family of discrete adiabatic algorithms. It is straightforward to port a standard adiabatic algorithm to an anholonomic adiabatic one, except an introduction of a parameter |v>, which is available to adjust the gaps of the quasienergies to control the running time steps. In Grover's database search problem, the costs to prepare |v> for the qualitatively different, i.e., power or exponential, running time steps are shown to be qualitatively different. Curiously, in establishing the equivalence between the standard quantum computation based on the circuit model and the anholonomic adiabatic quantum computation model, it is shown that the cost for |v> to enlarge the gaps of the eigenvalue is qualitatively negligible.Comment: 11 pages, 2 figure

On Equivalence of Critical Collapse of Non-Abelian Fields

We continue our study of the gravitational collapse of spherically symmetric skyrmions. For certain families of initial data, we find the discretely self-similar Type II critical transition characterized by the mass scaling exponent $\gamma \approx 0.20$ and the echoing period $\Delta \approx 0.74$. We argue that the coincidence of these critical exponents with those found previously in the Einstein-Yang-Mills model is not accidental but, in fact, the two models belong to the same universality class.Comment: 7 pages, REVTex, 2 figures included, accepted for publication in Physical Review

Syn5 RNA polymerase synthesizes precise run-off RNA products

The enzyme predominantly used for in vitro run-off RNA synthesis is bacteriophage T7 RNA polymerase. T7 RNA polymerase synthesizes, in addition to run-off products of precise length, transcripts with an additional non-base-paired nucleotide at the 3′-terminus (N + 1 product). This contaminating product is extremely difficult to remove. We recently characterized the single-subunit RNA polymerase from marine cyanophage Syn5 and identified its promoter sequence. This marine enzyme catalyses RNA synthesis over a wider range of temperature and salinity than does T7 RNA polymerase. Its processivity is >30 000 nt without significant intermediate products. The requirement for the initiating nucleotide at the promoter is less stringent for Syn5 RNA polymerase as compared to T7 RNA polymerase. A major difference is the precise run-off transcripts with homogeneous 3′-termini synthesized by Syn5 RNA polymerase. Therefore, the enzyme is advantageous for the production of RNAs that require precise 3′-termini, such as tRNAs and RNA fragments that are used for subsequent assembly

From simple to complex networks: inherent structures, barriers and valleys in the context of spin glasses

Given discrete degrees of freedom (spins) on a graph interacting via an energy function, what can be said about the energy local minima and associated inherent structures? Using the lid algorithm in the context of a spin glass energy function, we investigate the properties of the energy landscape for a variety of graph topologies. First, we find that the multiplicity Ns of the inherent structures generically has a lognormal distribution. In addition, the large volume limit of ln/ differs from unity, except for the Sherrington-Kirkpatrick model. Second, we find simple scaling laws for the growth of the height of the energy barrier between the two degenerate ground states and the size of the associated valleys. For finite connectivity models, changing the topology of the underlying graph does not modify qualitatively the energy landscape, but at the quantitative level the models can differ substantially.Comment: 10 pages, 9 figs, slightly improved presentation, more references, accepted for publication in Phys Rev

Microarray evidence for off target effects in tick RNA interference experiments, and the lack of strong correlation between DSRNA and antibody phenotypes in tick In vitro treatments for vaccine candidate screening

No abstract availabl

Brown Stem Rot and its Interaction with the Soybean Cyst Nematode

Brown stem rot (BSR) of soybeans is caused by the fungal vascular pathogen Cadophora gregata (previously named Phialophora gregata). BSR is an economically important disease of soybeans in the north central United States, being prevalent in 68 to 73% of the soybean fields of Illinois, Iowa, and Minnesota (Workneh et al. 1999). There are two genetic types (called genotypes) of C. gregata that differ in their ability to cause foliar symptoms on susceptible soybeans (Chen et al. 2000). Infection by genotype A of the fungus can result in mild to severe brown discoloration of the pith and severe foliar symptoms on susceptible soybeans and mild or no foliar symptom on resistant soybeans. In contrast, infection by genotype B of the fungus causes mild to severe brown discoloration of the pith, but mild or no foliar symptoms. Soybeans can be colonized by both genotypes of the fungus without exhibiting stem or foliar symptoms (Taboret al. 2003a). Consequently, hidden yield loss due to BSR may frequently occur

SeGMA: Semi-Supervised Gaussian Mixture Auto-Encoder

We propose a semi-supervised generative model, SeGMA, which learns a joint probability distribution of data and their classes and which is implemented in a typical Wasserstein auto-encoder framework. We choose a mixture of Gaussians as a target distribution in latent space, which provides a natural splitting of data into clusters. To connect Gaussian components with correct classes, we use a small amount of labeled data and a Gaussian classifier induced by the target distribution. SeGMA is optimized efficiently due to the use of Cramer-Wold distance as a maximum mean discrepancy penalty, which yields a closed-form expression for a mixture of spherical Gaussian components and thus obviates the need of sampling. While SeGMA preserves all properties of its semi-supervised predecessors and achieves at least as good generative performance on standard benchmark data sets, it presents additional features: (a) interpolation between any pair of points in the latent space produces realistically-looking samples; (b) combining the interpolation property with disentangled class and style variables, SeGMA is able to perform a continuous style transfer from one class to another; (c) it is possible to change the intensity of class characteristics in a data point by moving the latent representation of the data point away from specific Gaussian components