Participants’ perceptions and use of the contents of Working Together : an interpersonal communication skills approach to team building

Thesis (M.A.)--University of Kansas, Communication Studies, 1984

Skeletal muscle sodium glucose co-transporters in older adults with type 2 diabetes undergoing resistance training

We examined the expression of the sodium-dependent glucose co-transporter system (hSGLT3) in skeletal muscle of Hispanic older adults with type 2 diabetes. Subjects (65±8 yr) were randomized to resistance training (3x/wk, n=13) or standard of care (controls, n=5) for 16 weeks. Skeletal muscle hSGLT3 and GLUT4 mRNA transcript levels were determined by real time RT-PCR. hSGLT3 transcripts increased by a factor of ten following resistance training compared to control subjects (0.10, P=0.03). There were no differences in GLUT4 mRNA expression levels between groups. Protein expression levels of these transporters were confirmed by immunohistochemistry and Western blotting. hSGLT3 after resistance exercise was found not to be co-localized with the nicotinic acetylcholine receptor. The change in hSGLT3 transcript levels in the vastus lateralis muscle was positively correlated with glucose uptake, as measured by the change in muscle glycogen stores (r=0.53, P=0.02); and with exercise intensity, as measured by the change in muscle strength (r=0.73, P=0.001). Group assignment was be the only independent predictor of hSGLT3 transcript levels, explaining 68% of its variability (P=0.01). Our data show that hSGLT3, but not GLTU4, expression was enhanced in skeletal muscle after 16 weeks of resistance training. This finding suggests that hSGLT3, an insulin-independent glucose transporter, is activated with exercise and it may play a significant role in glycemic control with muscle contraction. The hSGLT3 exact mechanism is not well understood and requires further investigation. However its functional significance regarding a reduction of glucose toxicity and improvement of insulin resistance is the subject of ongoing research

Reduced Sampling for Construction of Quadratic Response Surface Approximations Using Adaptive Experimental Design

The purpose of this paper is to reduce the computational complexity per step from O(n^2) to O(n) for optimization based on quadratic surrogates, where n is the number of design variables. Applying nonlinear optimization strategies directly to complex multidisciplinary systems can be prohibitively expensive when the complexity of the simulation codes is large. Increasingly, response surface approximations, and specifically quadratic approximations, are being integrated with nonlinear optimizers in order to reduce the CPU time required for the optimization of complex multidisciplinary systems. For evaluation by the optimizer, response surface approximations provide a computationally inexpensive lower fidelity representation of the system performance. The curse of dimensionality is a major drawback in the implementation of these approximations as the amount of required data grows quadratically with the number n of design variables in the problem. In this paper a novel technique to reduce the magnitude of the sampling from O(n^2) to O(n) is presented. The technique uses prior information to approximate the eigenvectors of the Hessian matrix of the response surface approximation and only requires the eigenvalues to be computed by response surface techniques. The technique is implemented in a sequential approximate optimization algorithm and applied to engineering problems of variable size and characteristics. Results demonstrate that a reduction in the data required per step from O(n^2) to O(n) points can be accomplished without significantly compromising the performance of the optimization algorithm. A reduction in the time (number of system analyses) required per step from O(n^2) to O(n) is significant, even more so as n increases. The novelty lies in how only O(n) system analyses can be used to approximate a Hessian matrix whose estimation normally requires O(n^2) system analyses

Adaptations of frogs to survive freezing

Five species of frogs from North America survive extensive freezing of their body fluids to temperatures as low as -8C for periods lasting at least 2 weeks. These frogs hibernate in leaf litter where subzero temperatures commonly occur during the winter. The onset of freezing triggers liver glycogenolysis and the production of high concentrations of glucose or glycerol (to 100x normal) that functions as a cryoprotectant against freezing injury. Concomitantly the release of the latent heat of crystallization as body water freezes promotes the continued function of the cardiovascular system for many hours, and serves to distribute glucose throughout the body. The water content of major organs is reduced by 50% or more during the first 24 hours of freezing, with the water being relocated and frozen in other body spaces. Organ dehydration functions to concentrate cryoprotectant and the reduce mechanical damage by ice during freezing. As freezing progresses, breathing, heart beat, and most other vital functions cease, but reanimation occurs within a few hours after thawing. The evolution of freeze tolerance in these animals illustrates the highly flexible capacities of frogs to adapt to stressful environments

Postoperative Delirium Prevention in the Older Adult: An Evidence-Based Process Improvement Project

Postoperative delirium is a major complication in hospitalized older adults. Implementation of a screening tool and evidence-based delirium-prevention protocol on a surgical unit increased nurses’ knowledge regarding delirium, increased identification of delirium, and produced medical treatment alterations leading to positive patient outcomes

KKT conditions satisfied using adaptive neighboring in hybrid cellular automata for topology optimization

The hybrid cellular automaton (HCA) method is a biologically inspired algorithm capable of topology synthesis that was developed to simulate the behavior of the bone functional adaptation process. In this algorithm, the design domain is divided into cells with some communication property among neighbors. Local evolutionary rules, obtained from classical control theory, iteratively establish the value of the design variables in order to minimize the local error between a field variable and a corresponding target value. Karush-Kuhn-Tucker (KKT) optimality conditions have been derived to determine the expression for the field variable and its target. While averaging techniques mimicking intercellular communication have been used to mitigate numerical instabilities such as checkerboard patterns and mesh dependency, some questions have been raised whether KKT conditions are fully satisfied in the final topologies. Furthermore, the averaging procedure might result in cancellation or attenuation of the error between the field variable and its target. Several examples are presented showing that HCA converges to different final designs for different neighborhood configurations or averaging schemes. Although it has been claimed that these final designs are optimal, this might not be true in a precise mathematical sense—the use of the averaging procedure induces a mathematical incorrectness that has to be addressed. In this work, a new adaptive neighboring scheme will be employed that utilizes a weighting function for the influence of a cell’s neighbors that decreases to zero over time. When the weighting function reaches zero, the algorithm satisfies the aforementioned optimality criterion. Thus, the HCA algorithm will retain the benefits that result from utilizing neighborhood information, as well as obtain an optimal solution