Reaching those most in need: a review of diabetes self-management interventions in disadvantaged populations

There has been increased recognition of the importance of developing diabetes self-management education (DSME) interventions that are effective with under-served and minority populations. Despite several recent studies in this area, there is to our knowledge no systematic review or synthesis of what has been learned from this research. An electronic literature search identified five formative evaluations and ten controlled DSME intervention trials focused on under-served (low-income, minority or aged) populations. The RE-AIM (Reach, Efficacy, Adoption, Implementation, Maintenance) evaluation framework was used to evaluate the controlled studies on the dimensions of reach, efficacy, adoption, implementation, and maintenance. Fifty percent of the studies identified reported on the percentage of patients who participated, and the percentages were highly variable. The methodological quality of the articles was generally good and the short-term results were encouraging, especially on behavioral outcomes. Data on adoption (representativeness of settings and clinicians who participate) and implementation were almost never reported. Studies of modalities in addition to group meetings are needed to increase the reach of DSME with under-served populations. The promising formative evaluation work that has been conducted needs to be extended for more systematic study of the process of intervention implementation and adaptation with special populations. Studies that explicitly address the community context and that address multiple issues related to public health impact of DSME interventions are recommended to enhance long-term results. Copyright (C) 2002 John Wiley Sons, Ltd.

Large amplitude folding in finely layered viscoelastic rock structures

We analyze folding phenomena in finely layered viscoelastic rock. Fine is meant in the sense that the thickness of each layer is considerably smaller than characteristic structural dimensions. For this purpose we derive constitutive relations and apply a computational simulation scheme (a finite-element based particle advection scheme; see MORESI et al., 2001) suitable for problems involving very large deformations of layered viscous and viscoelastic rocks. An algorithm for the time integration of the governing equations as well as details of the finite-element implementation is also given. We then consider buckling instabilities in a finite, rectangular domain. Embedded within this domain, parallel to the longer dimension we consider a stiff, layered plate. The domain is compressed along the layer axis by prescribing velocities along the sides. First, for the viscous limit we consider the response to a series of harmonic perturbations of the director orientation. The Fourier spectra of the initial folding velocity are compared for different viscosity ratios. Turning to the nonlinear regime we analyze viscoelastic folding histories up to 40% shortening. The effect of layering manifests itself in that appreciable buckling instabilities are obtained at much lower viscosity ratios (1:10) as is required for the buckling of isotropic plates (1:500). The wavelength induced by the initial harmonic perturbation of the director orientation seems to be persistent. In the section of the parameter space considered here elasticity seems to delay or inhibit the occurrence of a second, larger wavelength. Finally, in a linear instability analysis we undertake a brief excursion into the potential role of couple stresses on the folding process. The linear instability analysis also provides insight into the expected modes of deformation at the onset of instability, and the different regimes of behavior one might expect to observe.

Ecological interface design for pasteurizer II: A process description of semantic mapping

Ecological interface design (EID) is proving to be a promising approach to the design of interfaces for complex dynamic systems. Although the principles of EID and examples of its effective use are widely available, few readily available examples exist of how the individual displays that constitute an ecological interface are developed. This paper presents the semantic mapping process within EID in the context of prior theoretical work in this area. The semantic mapping process that was used in developing an ecological interface for the Pasteurizer II microworld is outlined, and the results of an evaluation of the ecological interface against a more conventional interface are briefly presented. Subjective reports indicate features of the ecological interface that made it particularly valuable for participants. Finally, we outline the steps of an analytic process for using EID. The findings presented here can be applied in the design of ecological interfaces or of configural displays for dynamic processes.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Ecosystem process models at multiple scales for mapping tropical forest productivity

Quantifying mass and energy exchanges within tropical forests is essential for understanding their role in the global carbon budget and how they will respond to perturbations in climate. This study reviews ecosystem process models designed to predict the growth and productivity of temperate and tropical forest ecosystems. Temperate forest models were included because of the minimal number of tropical forest models. The review provides a multiscale assessment enabling potential users to select a model suited to the scale and type of information they require in tropical forests. Process models are reviewed in relation to their input and output parameters, minimum spatial and temporal units of operation, maximum spatial extent and time period of application for each organization level of modelling. Organizational levels included leaf-tree, plot-stand, regional and ecosystem levels, with model complexity decreasing as the time-step and spatial extent of model operation increases. All ecosystem models are simplified versions of reality and are typically aspatial. Remotely sensed data sets and derived products may be used to initialize, drive and validate ecosystem process models. At the simplest level, remotely sensed data are used to delimit location, extent and changes over time of vegetation communities. At a more advanced level, remotely sensed data products have been used to estimate key structural and biophysical properties associated with ecosystem processes in tropical and temperate forests. Combining ecological models and image data enables the development of carbon accounting systems that will contribute to understanding greenhouse gas budgets at biome and global scales.

Quantum process tomography of a controlled-NOT gate

We demonstrate complete characterization of a two-qubit entangling process-a linear optics controlled-NOT gate operating with coincident detection-by quantum process tomography. We use a maximum-likelihood estimation to convert the experimental data into a physical process matrix. The process matrix allows an accurate prediction of the operation of the gate for arbitrary input states and a calculation of gate performance measures such as the average gate fidelity, average purity, and entangling capability of our gate, which are 0.90, 0.83, and 0.73, respectively.

Extinction of affective learning: No evidence for dual process accounts of human learning

The acquisition and extinction of affective valence to neutral geometrical shape conditional stimuli was investigated in three experiments. Experiment 1 employed a differential conditioning procedure with aversive shock USs. Differential electrodermal responding was evident during acquisition and lost during extinction. As indexed by verbal ratings, the CS1 acquired negative valence during acquisition,which was reduced after extinction. Affective priming, a reaction time based demand free measure of stimulus valence, failed to provide evidence for affective learning. Experiment 2 employed pictures of happy and angry faces as USs.Valence ratings after acquisitionweremore positive for theCS paired with happy faces (CS-H) and less positive for the CS paired with angry faces (CS-A) than during baseline. Extinction training reduced the extent of acquired valence significantly for both CSs, however, ratings of the CS-A remained different from baseline. Affective priming confirmed these results yielding differences between CS-A and CS-H after acquisition for pleasant and unpleasant targets, but for pleasant targets only after extinction. Experiment 3 replicated the design of Experiment 2, but presented the US pictures backwardly masked. Neither rating nor affective priming measures yielded any evidence for affective learning. The present results confirm across two different experimental procedures that, contrary to predictions from dual process accounts of human learning, affective learning is subject to extinction.

Process development for a recombinant Chinese Hamster Ovary (CHO) cell line utilizing a metal induced and amplified metallothionein expression system

The suspension Chinese Hamster Ovary cell line, 13-10-302, utilizing the metallothionein (MT) expression system producing recombinant human growth hormone (hGH) was studied in a serum-free and cadmium-free medium at different fermentation scales and modes of operation. Initial experiments were carried out to optimize the concentration of metal addition to induce the MT promoter. Subsequently, the cultivation of the 13-10-302 cell line was scaled up from spinner flasks into bioreactors, and the cultivation duration was extended with fed-batch and perfusion strategies utilizing 180 muM zinc to induce the promoter controlling expression of recombinant hGH. It was shown that a fed-batch process could increase the maximum cell numbers twofold, from 3.3 to 6.3 x 10(6) cell/mL, over those obtained in normal batch fermentations, and this coupled with extended fermentation times resulted in a fourfold increase in final hGH titer, from 135 +/- 15 to 670 +/- 70 mg/L at a specific productivity q(hGH) value of 12 pg cell(-1)d(-1). The addition of sodium butyrate increased the specific productivity of hGH in cells to a value of approximately 48 pg cell(-1)d(-1), resulting in a final hGH titer of over a gram per liter during fed-batch runs. A BioSep acoustic cell recycler was used to retain the cells in the bioreactor during perfusion operation. It was necessary to maintain the specific feeding rates (SFR) above a value of 0.2 vvd/(10(6) cell/mL) to maintain the viability and productivity of the 13-10-302 cells; under these conditions the viable cell number increased to over 107 cell/mL and resulted in a volumetric productivity of over 120 mg(hGH) L(-1)d(-1). Process development described in this work demonstrates cultivation at various scales and sustained high levels of productivity under cadmium free condition in a CHO cell line utilizing an inducible metallothionein expression system. (C) 2004 Wiley Periodicals, Inc.

Analysis of dynamic process models for structural insight and model reduction .1. Structural identification measures

An important consideration in the development of mathematical models for dynamic simulation, is the identification of the appropriate mathematical structure. By building models with an efficient structure which is devoid of redundancy, it is possible to create simple, accurate and functional models. This leads not only to efficient simulation, but to a deeper understanding of the important dynamic relationships within the process. In this paper, a method is proposed for systematic model development for startup and shutdown simulation which is based on the identification of the essential process structure. The key tool in this analysis is the method of nonlinear perturbations for structural identification and model reduction. Starting from a detailed mathematical process description both singular and regular structural perturbations are detected. These techniques are then used to give insight into the system structure and where appropriate to eliminate superfluous model equations or reduce them to other forms. This process retains the ability to interpret the reduced order model in terms of the physico-chemical phenomena. Using this model reduction technique it is possible to attribute observable dynamics to particular unit operations within the process. This relationship then highlights the unit operations which must be accurately modelled in order to develop a robust plant model. The technique generates detailed insight into the dynamic structure of the models providing a basis for system re-design and dynamic analysis. The technique is illustrated on the modelling for an evaporator startup. Copyright (C) 1996 Elsevier Science Ltd

Robust algorithms for solving stochastic partial differential equations

A robust semi-implicit central partial difference algorithm for the numerical solution of coupled stochastic parabolic partial differential equations (PDEs) is described. This can be used for calculating correlation functions of systems of interacting stochastic fields. Such field equations can arise in the description of Hamiltonian and open systems in the physics of nonlinear processes, and may include multiplicative noise sources. The algorithm can be used for studying the properties of nonlinear quantum or classical field theories. The general approach is outlined and applied to a specific example, namely the quantum statistical fluctuations of ultra-short optical pulses in chi((2)) parametric waveguides. This example uses a non-diagonal coherent state representation, and correctly predicts the sub-shot noise level spectral fluctuations observed in homodyne detection measurements. It is expected that the methods used wilt be applicable for higher-order correlation functions and other physical problems as well. A stochastic differencing technique for reducing sampling errors is also introduced. This involves solving nonlinear stochastic parabolic PDEs in combination with a reference process, which uses the Wigner representation in the example presented here. A computer implementation on MIMD parallel architectures is discussed. (C) 1997 Academic Press.

The formal representation of process system modelling assumptions and their implications

In order to analyse the effect of modelling assumptions in a formal, rigorous way, a syntax of modelling assumptions has been defined. The syntax of modelling assumptions enables us to represent modelling assumptions as transformations acting on the set of model equations. The notion of syntactical correctness and semantical consistency of sets of modelling assumptions is defined and methods for checking them are described. It is shown on a simple example how different modelling assumptions act on the model equations and their effect on the differential index of the resulted model is also indicated.