Variation in dynamic elastic shear modulus of sandstone upon fluid saturation and substitution

Experimental acoustic measurements on sandstone rocks at both sonic and ultrasonic frequencies show that fluid saturation can cause a noticeable change in both the dynamic bulk and shear elastic moduli of sandstones. We observed that the change in dynamic shear modulus upon fluid saturation is highly dependent on the type of saturant, its viscosity, rock microstructure, and applied pressures. Frequency dispersion has some influence on dynamic elastic moduli too, but its effect is limited to the ultrasonic frequency ranges and above. We propose that viscous coupling, reduction in free surface energy, and, to a limited extent, frequency dispersion due to both local and global flow are the main mechanisms responsible for the change in dynamic shear elastic modulus upon fluid saturation and substitution, and we quantify influences.

Structure and biogenesis of the capsular F1 antigen from Yersinia pestis: Preserved folding energy drives fiber formation

Most gram-negative pathogens express fibrous adhesive virulence organelles that mediate targeting to the sites of infection. The F1 capsular antigen from the plague pathogen Yersinia pestis consists of linear fibers of a single subunit (Caf1) and serves as a prototype for nonpilus organelles assembled via the chaperone/usher pathway. Genetic data together with high-resolution X-ray structures corresponding to snapshots of the assembly process reveal the structural basis of fiber formation. Comparison of chaperone bound Caf1 subunit with the subunit in the fiber reveals a novel type of conformational change involving the entire hydrophobic core of the protein. The observed conformational change suggests that the chaperone traps a high-energy folding intermediate of Caf1. A model is proposed in which release of the subunit allows folding to be completed, driving fiber formation.

Streptococcus castoreus sp nov., isolated from a beaver (Castor fiber)

A previously undescribed, Gram-positive, catalase-negative, Streptococcus-like organism originating from a European beaver (Castor fiber) was subjected to a taxonomic study. The organism displayed beta-haemolytic activity and gave a positive reaction with Lancefield group A antisera. Based on the results of biochemical testing, the organism was tentatively identified as a member of the genus Streptococcus, but it did not correspond phenotypically to any recognized species of this genus. Comparative 16S rRNA gene sequencing studies confirmed this assignment, with the bacterium forming a hitherto unknown subline within the genus. Sequence divergence values of greater than 3% from other reference streptococcal species, however, demonstrated that the unidentified coccus-shaped organism represents a hitherto unknown species. Based on phenotypic and molecular phylogenetic evidence, it is therefore proposed that the unknown organism from a beaver be classified as a novel species, Streptococcus castoreus sp. nov. The type strain is M605815/03/2(T) (=CCUG 48115(T) = CIP 108205(T)).

Mucoadhesive and elastic films based on blends of chitosan and hydroxyethylcellulose

Mucoadhesive polymeric films have been prepared based on blends of chitosan and hydroxyethylcellulose. The blends have been characterized by IR spectroscopy, DSC, WAXD, TGA, SEM, and mechanical testing. It is demonstrated that the mechanical properties of chitosan are improved significantly upon blending with hydroxyethylcellulose. An increase in hydroxyethylcellulose content in the blends makes the materials more elastic. The thermal treatment of the blends at 100 degrees C leads to partial cross-linking of the polymers and formation of water-insoluble but swellable materials. The adhesion of the films towards porcine buccal mucosa decreases with increasing hydroxyethylcellulose content in the blends.

Planning rigid body motions using elastic curves

This paper tackles the problem of computing smooth, optimal trajectories on the Euclidean group of motions SE(3). The problem is formulated as an optimal control problem where the cost function to be minimized is equal to the integral of the classical curvature squared. This problem is analogous to the elastic problem from differential geometry and thus the resulting rigid body motions will trace elastic curves. An application of the Maximum Principle to this optimal control problem shifts the emphasis to the language of symplectic geometry and to the associated Hamiltonian formalism. This results in a system of first order differential equations that yield coordinate free necessary conditions for optimality for these curves. From these necessary conditions we identify an integrable case and these particular set of curves are solved analytically. These analytic solutions provide interpolating curves between an initial given position and orientation and a desired position and orientation that would be useful in motion planning for systems such as robotic manipulators and autonomous-oriented vehicles.

Neuromuscular junction morphology, fiber-type proportions, and satellite-cell proliferation rates are altered in MyoD(-/-) mice

Gene compensation by members of the myogenic regulatory factor (MRF) family has been proposed to explain the apparent normal adult phenotype of MyoD(-/-) mice. Nerve and field stimulation were used to investigate contraction properties of muscle from MyoD(-/-) mice, and molecular approaches were used to investigate satellite-cell behavior. We demonstrate that MyoD deletion results in major alterations in the organization of the neuromuscular junction, which have a dramatic influence on the physiological contractile properties of skeletal muscle. Second, we show that the lineage progression of satellite cells (especially initial proliferation) in the absence of MyoD is abnormal and linked to perturbations in the nuclear localization of beta-catenin, a key readout of canonical Wnt signaling. These results show that MyoD has unique functions in both developing and adult skeletal muscle that are not carried out by other members of the MRF family.

Radiometric analysis of the light coupled by optimally cut plastic optical fiber amplitude modulating reflectance displacement sensors

A radiometric analysis of the light coupled by optical fiber amplitude modulating extrinsic-type reflectance displacement sensors is presented. Uncut fiber sensors show the largest range but a smaller responsivity. Single cut fiber sensors exhibit an improvement in responsivity at the expense of range. A further increase in responsivity as well as a reduction in the operational range is obtained when the double cut sensor configuration is implemented. The double cut configuration is particularly suitable in applications where feedback action is applied to the moving reflector surface. © 2000 American Institute of Physics.

Prebiotics and the health benefits of fiber: current regulatory status, future research, and goals.

First defined in the mid-1990s, prebiotics, which alter the composition and activity of gastrointestinal (GI) microbiota to improve health and well-being, have generated scientific and consumer interest and regulatory debate. The Life Sciences Research Organization, Inc. (LSRO) held a workshop, Prebiotics and the Health Benefits of Fiber: Future Research and Goals, in February 2011 to assess the current state of the science and the international regulatory environment for prebiotics, identify research gaps, and create a strategy for future research. A developing body of evidence supports a role for prebiotics in reducing the risk and severity of GI infection and inflammation, including diarrhea, inflammatory bowel disease, and ulcerative colitis as well as bowel function disorders, including irritable bowel syndrome. Prebiotics also increase the bioavailability and uptake of minerals and data suggest that they reduce the risk of obesity by promoting satiety and weight loss. Additional research is needed to define the relationship between the consumption of different prebiotics and improvement of human health. New information derived from the characterization of the composition and function of different prebiotics as well as the interactions among and between gut microbiota and the human host would improve our understanding of the effects of prebiotics on health and disease and could assist in surmounting regulatory issues related to prebiotic use.

An elastic net orthogonal forward regression algorithm

In this paper we propose an efficient two-level model identification method for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularization parameters in the elastic net are optimized using a particle swarm optimization (PSO) algorithm at the upper level by minimizing the leave one out (LOO) mean square error (LOOMSE). Illustrative examples are included to demonstrate the effectiveness of the new approaches.

(Non-)ergodicity of a degenerate diffusion modeling the fiber lay down process

We analyze the large time behavior of a stochastic model for the lay down of fibers on a moving conveyor belt in the production process of nonwovens. It is shown that under weak conditions this degenerate diffusion process has a unique invariant distribution and is even geometrically ergodic. This generalizes results from previous works [M. Grothaus and A. Klar, SIAM J. Math. Anal., 40 (2008), pp. 968–983; J. Dolbeault et al., arXiv:1201.2156] concerning the case of a stationary conveyor belt, in which the situation of a moving conveyor belt has been left open.

Elastic-net prefiltering for two-class classification

A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage which constructs a sparse linear-in-the-parameter classifier. The prefiltering stage is a two-level process aimed at maximizing a model's generalization capability, in which a new elastic-net model identification algorithm using singular value decomposition is employed at the lower level, and then, two regularization parameters are optimized using a particle-swarm-optimization algorithm at the upper level by minimizing the leave-one-out (LOO) misclassification rate. It is shown that the LOO misclassification rate based on the resultant prefiltered signal can be analytically computed without splitting the data set, and the associated computational cost is minimal due to orthogonality. The second stage of sparse classifier construction is based on orthogonal forward regression with the D-optimality algorithm. Extensive simulations of this approach for noisy data sets illustrate the competitiveness of this approach to classification of noisy data problems.