Evaluation of SOVAT: An OLAP-GIS decision support system for community health assessment data analysis

Background. Data analysis in community health assessment (CHA) involves the collection, integration, and analysis of large numerical and spatial data sets in order to identify health priorities. Geographic Information Systems (GIS) enable for management and analysis using spatial data, but have limitations in performing analysis of numerical data because of its traditional database architecture. On-Line Analytical Processing (OLAP) is a multidimensional datawarehouse designed to facilitate querying of large numerical data. Coupling the spatial capabilities of GIS with the numerical analysis of OLAP, might enhance CHA data analysis. OLAP-GIS systems have been developed by university researchers and corporations, yet their potential for CHA data analysis is not well understood. To evaluate the potential of an OLAP-GIS decision support system for CHA problem solving, we compared OLAP-GIS to the standard information technology (IT) currently used by many public health professionals. Methods. SOVAT, an OLAP-GIS decision support system developed at the University of Pittsburgh, was compared against current IT for data analysis for CHA. For this study, current IT was considered the combined use of SPSS and GIS ("SPSS-GIS"). Graduate students, researchers, and faculty in the health sciences at the University of Pittsburgh were recruited. Each round consisted of: an instructional video of the system being evaluated, two practice tasks, five assessment tasks, and one post-study questionnaire. Objective and subjective measurement included: task completion time, success in answering the tasks, and system satisfaction. Results. Thirteen individuals participated. Inferential statistics were analyzed using linear mixed model analysis. SOVAT was statistically significant (α = .01) from SPSS-GIS for satisfaction and time (p < .002). Descriptive results indicated that participants had greater success in answering the tasks when using SOVAT as compared to SPSS-GIS. Conclusion. Using SOVAT, tasks were completed more efficiently, with a higher rate of success, and with greater satisfaction, than the combined use of SPSS and GIS. The results from this study indicate a potential for OLAP-GIS decision support systems as a valuable tool for CHA data analysis. © 2008 Scotch et al; licensee BioMed Central Ltd

Hypofractionated radiotherapy for prostate cancer

In the last few years, hypofractionated external beam radiotherapy has gained increasing popularity for prostate cancer treatment, since sufficient evidence exists that prostate cancer has a low alpha/beta ratio, lower than the one of the surrounding organs at risk and thus there is a potential therapeutic benefit of using larger fractionated single doses. Apart from the therapeutic rationale there are advantages such as saving treatment time and medical resources and thereby improving patient's convenience. While older trials showed unsatisfactory results in both standard and hypofractionated arm due to insufficient radiation doses and non-standard contouring of target volumes, contemporary randomized studies have reported on encouraging results of tumor control mostly without an increase of relevant side effects, especially late toxicity. Aim of this review is to give a detailed analysis of relevant, recently published clinical trials with special focus on rationale for hypofractionation and different therapy settings

Grid Cells, Place Cells, and Geodesic Generalization for Spatial Reinforcement Learning

Reinforcement learning (RL) provides an influential characterization of the brain's mechanisms for learning to make advantageous choices. An important problem, though, is how complex tasks can be represented in a way that enables efficient learning. We consider this problem through the lens of spatial navigation, examining how two of the brain's location representations—hippocampal place cells and entorhinal grid cells—are adapted to serve as basis functions for approximating value over space for RL. Although much previous work has focused on these systems' roles in combining upstream sensory cues to track location, revisiting these representations with a focus on how they support this downstream decision function offers complementary insights into their characteristics. Rather than localization, the key problem in learning is generalization between past and present situations, which may not match perfectly. Accordingly, although neural populations collectively offer a precise representation of position, our simulations of navigational tasks verify the suggestion that RL gains efficiency from the more diffuse tuning of individual neurons, which allows learning about rewards to generalize over longer distances given fewer training experiences. However, work on generalization in RL suggests the underlying representation should respect the environment's layout. In particular, although it is often assumed that neurons track location in Euclidean coordinates (that a place cell's activity declines “as the crow flies” away from its peak), the relevant metric for value is geodesic: the distance along a path, around any obstacles. We formalize this intuition and present simulations showing how Euclidean, but not geodesic, representations can interfere with RL by generalizing inappropriately across barriers. Our proposal that place and grid responses should be modulated by geodesic distances suggests novel predictions about how obstacles should affect spatial firing fields, which provides a new viewpoint on data concerning both spatial codes

Nanostructured composites based on carbon nanotubes and epoxy resin for use as radar absorbing materials

Nanostructured polymer composites have opened up new perspectives for multifunctional materials. In particular, carbon nanotubes (CNTs) present potential applications in order to improve mechanical and electrical performance in composites with aerospace application. The combination of epoxy resin with multiwalled carbon nanotubes results in a new functional material with enhanced electromagnetic properties. The objective of this work was the processing of radar absorbing materials based on formulations containing different quantities of carbon nanotubes in an epoxy resin matrix. To reach this objective the adequate concentration of CNTs in the resin matrix was determined. The processed structures were characterized by scanning electron microscopy, rheology, thermal and reflectivity in the frequency range of 8.2 to 12.4 GHz analyses. The microwave attenuation was up to 99.7%, using only 0.5% (w/w) of CNT, showing that these materials present advantages in performance associated with low additive concentrations.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Aeronaut Inst Technol ITA, Sao Jose Dos Campos, SP, BrazilInst Aeronaut & Space, Div Mat, Sao Jose Dos Campos, SP, BrazilUniv Estadual Paulista, UNESP, Dept Mat & Technol DMT, Guaratingueta, SP, BrazilUniv Estadual Paulista, UNESP, Dept Mat & Technol DMT, Guaratingueta, SP, BrazilCNPq: 305478/2009-5CNPq: 559246/2008-0CNPq: 151803/2008-0CNPq: 151154/2009-

Learning and generalization under ambiguity: an fMRI study.

Adaptive behavior often exploits generalizations from past experience by applying them judiciously in new situations. This requires a means of quantifying the relative importance of prior experience and current information, so they can be balanced optimally. In this study, we ask whether the brain generalizes in an optimal way. Specifically, we used Bayesian learning theory and fMRI to test whether neuronal responses reflect context-sensitive changes in ambiguity or uncertainty about experience-dependent beliefs. We found that the hippocampus expresses clear ambiguity-dependent responses that are associated with an augmented rate of learning. These findings suggest candidate neuronal systems that may be involved in aberrations of generalization, such as over-confidence

Main factors that affect the economic efficiency of broiler breeder production

This study aimed at identifying the factors that affect the economic efficiency of broiler breeder production using the analysis of stochastic profit frontier function. Data were collected in 48 broiler breeder farms contracted by a commercial company located in southwestern Paraná, Brazil. The collected data refer to the last batch of fertile eggs that was delivered to the company, between January, 2008, and July, 2009. The following parameters were evaluated: production of hatching eggs per hen (number of eggs/hen), hatchability (hatch %), feed intake per hatching egg (g feed/ egg), production scale (number of birds/batch), farmer's experience in production activities, and labor type. Factors, such as area of occupied land, electricity costs, and invested capital were also evaluated. Results showed that the cost of electricity, as well as area of occupied land, production scale, and feed intake per hatching egg significantly affect the economic efficiency of the broiler breeder farms in Southwestern Paraná, Brazil