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This article presents a framework that can be used to design, implement, and evaluate simulations used for teaching strategies in nursing education. Components of the framework include best practices in education, student factors, teacher factors, simulation design characteristics, and outcomes. Variables are identified for each of the framework components.
In this study, design optimization of a tooth implant-supported fixed prosthesis was investigated theoretically. A three-dimensional finite element analysis was utilized to simulate the stress distribution and deformation, with an emphasis on the material selection for various parts of the prosthesis. This mandibular prosthesis was supported by six implants. The properties of 3 different materials for implants and 4 different materials for framework were incorporated into 12 different models. For the loading conditions used, it was found that the largest displacements occurred at the far ends of the framework and that the resulting deflection was highly dependent on the material properties of the framework. The simulations showed that the stress in the framework was mainly concentrated near the holes in the lower surface and that the highest values of von Mises stresses occurred in the lingual part of the prosthesis. Furthermore, the modeling results revealed that more rigid frameworks led to a corresponding decreased stress in the retaining screws and that high-stress concentration areas moved from the neck of the implant towards the base of it, as the value of Young's modulus increased. It was concluded that the first best model was the Cr-Co alloy for the framework and the Ti alloy for the implant and the second best choice was the Cr-Co alloy for both the framework and the implant.
This paper describes an authoritative, non-proprietary information resource that provides an efficient mechanism for embedding specialized clinical knowledge into the design of healthcare telecommunications systems. The resource marries two types of data interchange standards, a message/electronic-document standard and a terminology standard. In technical terms, it is part protocol and part database. Industry, academia, professional specialty societies, and the federal government participated in its development. The development of multi-specialty content has broadly engaged biomedical domain experts to an unprecedented degree in voluntary, non-proprietary message/document-standards development. The resource is the SNOMED DICOM Microglossary (SDM), a message-terminology (or document-content) mapping resource. The message/electronic-document standard is DICOM (Digital Imaging and Communications in Medicine). The terminology standard is SNOMED, (Systematized Nomenclature of Human and Veterinary Medicine). The SDM specifies the mapping of multi-specialty imaging terminology from SNOMED to DICOM data elements. DICOM provides semantic constraints and a framework for discourse that are lacking in SNOMED. Thus the message standard and the computer-based terminology both depend upon and complete each other. The combination is synergistic. By substitution of different templates of specialty terminology from the SDM, a generic message template, such as the DICOM Visible Light (Color Diagnostic) Image or the DICOM Structured Reporting specification can be reconfigured for diverse applications. Professional societies, with technical assistance from the College of American Pathologists, contribute and maintain their portions of the terminology, and can use SDM templates and term lists in clinical practice guidelines for the structure and content of computer-based patient records.
This article describes the development and pilot testing of a computer-based, multiple-behavior obesity prevention program for adolescents. Using the Transtheoretical Model as a framework, this intervention offers individualized feedback based on readiness to engage in physical activity, to consume fruits and vegetables, and to limit television viewing. Focus groups and interviews with students, teachers, school administrators, and experts guided the development. Forty-five students participated in a baseline intervention session and completed a 16-item acceptability measure. Ratings were positive, with item means ranging from 3.60-4.75 on a 5-point scale. Student responses to open-ended questions aided in the enhancement of the intervention, for which an effectiveness trial begins in September 2006. This formative work demonstrated the acceptability of this school-based intervention approach, which can be promoted and prescribed by school nurses. Further, if found effective, it can be disseminated as an efficient, low-cost, population-based approach designed to address the epidemic of obesity.
Signal transducer and actuator of transcription (STATs) are a family of transcription factors activated by various cytokines, growth factors and hormones. They are important mediators of immune responses and growth and differentiation of various cell types. The STAT signalling system represents a defined functional module with a pattern of signalling that is conserved from flies to mammals. In order to probe and gain insights into the signalling properties of the STAT module by computational means, we developed a simple non-linear ordinary differential equations model within the 'Virtual Cell' framework. Our results demonstrate that the STAT module can operate as a 'biphasic amplitude filter' with an ability to amplify input signals within a specific intermediate range. We show that dimerisation of phosphorylated STAT is crucial for signal amplification and the amplitude filtering function. We also demonstrate that maximal amplification at intermediate levels of STAT activation is a moderately robust property of STAT module. We propose that these observations can be extrapolated to the analogous SMAD signalling module.
Identification of spatiotemporal interactions within/between neuron populations is critical for detection and characterization of large-scale neuronal interactions underlying perception, cognition, and behavior. Univariate analysis has been employed successfully in many neuroimaging studies. However, univariate analysis does not explicitly test for interactions between distributed areas of activity and is not sensitive to distributed responses across the brain. Multivariate analysis can explicitly test for multiple statistical models, including the designed paradigm, and allows for spatial and temporal model detection. Here, we investigate multivariate analysis approaches that take into consideration the 4D (time and space) covariance structure of the data. Principal component analysis (PCA) and independent component analysis (ICA) are two popular multivariate approaches with distinct mathematical constraints. Common difficulties in using these two different decompositions include the following: classification of the revealed components (task-related signal versus noise), overall signal-to-noise sensitivity, and the relatively low computational efficiency (multivariate analysis requires the entire raw data set and more time for model identification analysis). Using both Monte Carlo simulations and empirical data, we derived and tested the generalized partial least squares (gPLS) framework, which can incorporate both PCA and ICA decompositions with computational efficiency. The gPLS method explicitly incorporates the experimental design to simplify the identification of characteristic spatiotemporal patterns. We performed parametric modeling studies of a blocked-design experiment under various conditions, including background noise distribution, sampling rate, and hemodynamic response delay. We used a randomized grouping approach to manipulate the degrees of freedom of PCA and ICA in gPLS to characterize both paradigm coherent and transient brain responses. Simulation data suggest that in the gPLS framework, PCA mostly outperforms ICA as measured by the receiver operating curves (ROCs) in SNR from 0.01 to 100, the hemodynamic response delays from 0 to 3 TR in fMRI, background noise models of Guassian, sub-Gaussian, and super-Gaussian distributions and the number of observations from 5, 10, to 20 in each block of a six-block experiment. Further, due to selective averaging, the gPLS method performs robustly in low signal-to-noise ratio (<1) experiments. We also tested PCA and ICA using PLS in a simulated event-related fMRI data to show their similar detection. Finally, we tested our gPLS approach on empirical fMRI motor data. Using the randomized grouping method, we are able to identify both transient responses and consistent paradigm/model coherent components in the 10-epoch block design motor fMRI experiment. Overall, studies of synthetic and empirical data suggest that PLS analysis, using PCA decomposition, provides a stable and powerful tool for exploration of fMRI/behavior data.
BACKGROUND: This article proposes a conceptual framework for addressing social contextual factors in cancer prevention interventions, and describes work that operationalizes this model in interventions for working class, multiethnic populations. METHODS: The Harvard Cancer Prevention Program Project Includes Three Studies: (1) an intervention study in 25 small businesses; (2) an intervention study in 10 health centers; and (3) a computer simulation modeling project that translates risk factor modifications into gains in life expectancy and number of cancers averted. The conceptual framework guiding this work articulates pathways by which social context may influence health behaviors, and is used to frame the interventions and guide evaluation design. RESULTS: Social contextual factors cut across multiple levels of influence, and include individual factors (e.g., material circumstances, psychosocial factors), interpersonal factors (e.g., social ties, roles/responsibilities, social norms), organizational factors (e.g., work organization, access to health care), and neighborhood/community factors (e.g., safety, access to grocery stores). Social context is shaped by sociodemographic characteristics (e.g., social class, race/ethnicity, gender, age, language) that impact day-to-day realities. CONCLUSIONS: By illuminating the pathways by which social contextual factors influence health behaviors, it will be possible to enhance the effectiveness of interventions aimed at reducing social inequalities in risk behaviors.
This article presents both the rationale behind and a framework for incorporating pediatric preventive cardiology into undergraduate medical education and pediatric residency training. Such education of physicians in the practice of preventive cardiology requires provision of not only didactic information but also training and practice in clinical skills. A combination of didactic lectures, small group discussions, interactive programs utilizing video tapes and computer programs, family and patient simulation, preventive cardiology clinics, and community cardiovascular health promotion activities all help to provide the optimal framework for an effective medical student and pediatric resident training program in pediatric preventive cardiology. Since pediatricians are in the best position to practice true primary prevention of cardiovascular disease beginning in childhood, it is essential that physicians in training understand the concepts and learn the skills needed to practice this critical component of pediatric medicine.
In the whiplash polymerase chain reaction (WPCR), autonomous molecular computation is implemented in vitro by the recursive, self-directed polymerase extension of a mixture of DNA hairpins. Although computational efficiency is known to be reduced by a tendency for DNAs to self-inhibit by backhybridization, both the magnitude of this effect and its dependence on the reaction conditions have remained open questions. In this paper, the impact of backhybridization on WPCR efficiency is addressed by modeling the recursive extension of each strand as a Markov chain. The extension efficiency per effective polymerase-DNA encounter is then estimated within the framework of a statistical thermodynamic model. Model predictions are shown to provide close agreement with the premature halting of computation reported in a recent in vitro WPCR implementation, a particularly significant result, given that backhybridization had been discounted as the dominant error process. The scaling behavior further indicates completion times to be sufficiently long to render WPCR-based massive parallelism infeasible. A modified architecture, PNA-mediated WPCR (PWPCR) is then proposed in which the occupancy of backhybridized hairpins is reduced by targeted PNA(2)/DNA triplex formation. The efficiency of PWPCR is discussed using a modified form of the model developed for WPCR. Predictions indicate the PWPCR efficiency is sufficient to allow the implementation of autonomous molecular computation on a massive scale.
Computer technology is increasingly becoming an effective tool in restorative dentistry. Whether chairside or laboratory-fabricated, computer-aided design/ computer-aided manufacturing (CAD/CAM) engineering has become widely accepted in the fabrication of dental restorations. Zirconia has been successfully used in medical therapy and is now considered a viable alternative to metal as a substrate in single- and multiple-unit fixed restorations. Proper tooth preparation and handling of this material is essential for a successful outcome. This presentation will discuss how CAD/CAM systems can utilize zirconia frameworks to provide treatment of a maxillary anterior restoration.
Consistency-based diagnosis is one of the most widely used approaches to model-based diagnosis within the artificial intelligence community. It is usually carried out through an iterative cycle of behavior prediction, conflict detection, candidate generation, and candidate refinement. In that process conflict detection has proven to be a nontrivial step from the theoretical point of view. For this reason, many approaches to consistency-based diagnosis have relied upon some kind of dependency-recording. These techniques have had different problems, specially when they were applied to diagnose dynamic systems. Recently, offline dependency compilation has established itself as a suitable alternative approach to online dependency-recording. In this paper we propose the possible conflict concept as a compilation technique for consistency-based diagnosis. Each possible conflict represents a subsystem within system description containing minimal analytical redundancy and being capable to become a conflict. Moreover, the whole set of possible conflicts can be computed offline with no model evaluation. Once we have formalized the possible conflict concept, we explain how possible conflicts can be used in the consistency-based diagnosis framework, and how this concept can be easily extended to diagnose dynamic systems. Finally, we analyze its relation to conflicts in the general diagnosis engine (GDE) framework and compare possible conflicts with other compilation techniques, especially with analytical redundancy relations (ARRs) obtained through structural analysis. Based on results from these comparisons we provide additional insights in the work carried out within the BRIDGE community to provide a common framework for model-based diagnosis for both artificial intelligence and control engineering approaches.
Myocardial positron emission tomography (PET) with 18F-Fluordeoxyglucose (FDG) is increasingly used for the detection of viable tissue in the infarcted myocardium. Previous studies show that the variable metabolic conditions determine the regional distribution of this tracer and that the inhomogeneities of uptake often observed even in the normal myocardium may relate to substrate availability. The authors tried to stimulate the myocardial FDG uptake by either the technically easier method of glucose loading or by the euglycemic hyperinsulinemic clamp (EHC) technique. In their hands both methods could be considered as equally practicable but differing in some important details in regard to both the study protocol (tracer dose, optimal scanning time) and the reproducibility of results. The EHC allows a quick stabilization of the metabolic environment and resulted in an earlier and markedly increased FDG uptake. However, the important standardization of the method was performed by a computer-controlled system only for the glucose and insulin infusions. Their experiences show that the EHC provides a useful framework for assessing altered cardiac metabolism and possibly describes changes after therapeutic interventions more precisely than the commonly used glucose-loading technique.
A key issue in managing today's large amounts of genetic data is the availability of efficient, accurate, and selective techniques for detecting homologies (similarities) between newly discovered and already stored sequences. A common characteristic of today's most advanced algorithms, such as FASTA, BLAST, and BLAZE is the need to scan the contents of the entire database, in order to find one or more matches. This design decision results in either excessively long search times or, as is the case of BLAST, in a sharp trade-off between the achieved accuracy and the required amount of computation. The homology detection algorithm presented in this paper, on the other hand, is based on a probabilistic indexing framework. The algorithm requires minimal access to the database in order to determine matches. This minimal requirement is achieved by using the sequences of interest to generate a highly redundant number of very descriptive tuples; these tuples are subsequently used as indices in a table look-up paradigm. In addition to the description of the algorithm, theoretical and experimental results on the sensitivity and accuracy of the suggested approach are provided. The storage and computational requirements are described and the probability of correct matches and false alarms is derived. Sensitivity and accuracy are shown to be close to those of dynamic programming techniques. A prototype system has been implemented using the described ideas. It contains the full Swiss-Prot database rel 25 (10 MR) and the genome of E. Coli (2 MR). The system is currently being expanded to include the complete Genbank database.(ABSTRACT TRUNCATED AT 250 WORDS)
BACKGROUND: Health care systems will integrate new computing paradigms in the coming years. Context-awareness computing is a research field which often refers to health care as an interesting and rich area of application. AIM: Through a survey of the research literature, we intended to derive an objective view of the actual dynamism of context awareness in health care, and to identify strengths and weaknesses in this field. METHODS: After discussing definitions of context, we proposed a simple framework to analyse and characterize the use of context through three main axes. We then focused on context-awareness computing and reported on the main teams working in this area. We described some of the context-awareness projects in health care. A deeper analysis of the hospital-based projects demonstrated the gap between recommendations expressed for modelling context awareness and the actual use in a prototype. Finally, we identified pitfalls encountered in this area of research. RESULTS: A number of opportunities remain for this evolving field of research. We found relatively few groups with such a specific focus. As yet there is no consensus as to the most appropriate models or attributes to include in context awareness. We conclude that a greater understanding of which aspects of context are important in a health care setting is required; the inherent sociotechnical nature of context-aware applications in health care; and the need to draw on a number of disciplines to conduct this research.
The Munich Information Center for Protein Sequences (MIPS at the GSF), Neuherberg, Germany, provides resources related to genome information. Manually curated databases for several reference organisms are maintained. Several of these databases are described elsewhere in this and other recent NAR database issues. In a complementary effort, a comprehensive set of >400 genomes automatically annotated with the PEDANT system are maintained. The main goal of our current work on creating and maintaining genome databases is to extend gene centered information to information on interactions within a generic comprehensive framework. We have concentrated our efforts along three lines (i) the development of suitable comprehensive data structures and database technology, communication and query tools to include a wide range of different types of information enabling the representation of complex information such as functional modules or networks Genome Research Environment System, (ii) the development of databases covering computable information such as the basic evolutionary relations among all genes, namely SIMAP, the sequence similarity matrix and the CABiNet network analysis framework and (iii) the compilation and manual annotation of information related to interactions such as protein-protein interactions or other types of relations (e.g. MPCDB, MPPI, CYGD). All databases described and the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.gsf.de).
We study dynamical properties of ionic species in aqueous solutions of dodecyltrimethylammonium bromide, for several concentrations below and above the critical micellar concentration (cmc). New experimental determinations of the electrical conductivity are given which are compared to results obtained from an analytical transport theory; transport coefficients of ions in these solutions above the cmc are also computed from Brownian dynamics simulations. Analytical calculations as well as the simulation treat the solution within the framework of the continuous solvent model. Above the cmc, three ionic species are considered: the monomer surfactant, the micelle and the counterion. The analytical transport theory describes the structural properties of the electrolyte solution within the mean spherical approximation and assumes that the dominant forces which determine the deviations of transport processes from the ideal behavior (i.e., without any interactions between ions) are hydrodynamic interactions and electrostatic relaxation forces. In the simulations, both direct interactions and hydrodynamic interactions between solutes are taken into account. The interaction potential is modeled by pairwise repulsive 1/r(12) interactions and Coulomb interactions. The input parameters of the simulation (radii and self-diffusion coefficients of ions at infinite dilution) are partially obtained from the analytical transport theory which fits the experimental determinations of the electrical conductivity. Both the electrical conductivity of the solution and the self-diffusion coefficients of each species computed from Brownian dynamics are compared to available experimental data. In every case, the influence of hydrodynamic interactions (HIs) on the transport coefficients is investigated. It is shown that HIs are crucial to obtain agreement with experiments. In particular, the self-diffusion coefficient of the micelle, which is the largest and most charged species in the present system, is enhanced when HIs are included whereas the diffusion coefficients of the monomer and the counterion are roughly not influenced by HIs.
A computational model of how single neurons in and around the rat hippocampus support spatial navigation is reviewed. The extension of this model, to include the retrieval from human long-term memory of spatial scenes and the spatial context of events is discussed. The model explores the link between spatial and mnemonic functions by supposing that retrieval of spatial information from long-term storage requires the imposition of a particular viewpoint. It is consistent with data relating to representational hemispatial neglect and the involvement of the mammillary bodies, anterior thalamus, and hippocampal formation in supporting both episodic recall and the representation of head direction. Some recent behavioural, neuropsychological, and functional neuroimaging experiments are reviewed, in which virtual reality is used to allow controlled study of navigation and memory for events set within a rich large-scale spatial context. These studies provide convergent evidence that the human hippocampus is involved in both tasks, with some lateralization of function (navigation on the right and episodic memory on the left). A further experiment indicates hippocampal involvement in retrieval of spatial information from a shifted viewpoint. I speculate that the hippocampal role in episodic recollection relates to its ability to represent a viewpoint moving within a spatial framework.
A matrix representation of the evolution operator associated with a nonlinear stochastic flow with additive noise is used to compute its spectrum. In the weak noise limit a perturbative expansion for the spectrum is formulated in terms of local matrix representations of the evolution operator centered on classical periodic orbits. The evaluation of perturbative corrections is easier to implement in this framework than in the standard Feynman diagram perturbation theory. The results are perturbative corrections to a stochastic analog of the Gutzwiller semiclassical spectral determinant computed to several orders beyond what has so far been attainable in stochastic and quantum-mechanical applications.