The relationship between bioelectrical impedance phase angle and subjective global assessment in advanced colorectal cancer

Background  

Bioelectrical Impedance (BIA) derived phase angle is increasingly being used as an objective indicator of nutritional status in advanced cancer. Subjective Global Assessment (SGA) is a subjective method of nutritional status. The objective of this study was to investigate the association between BIA derived phase angle and SGA in advanced colorectal cancer.     

Youth gambling behaviors: An examination of the role of resilience.

The study of resilient children has overturned many deficit-focused models concerning the ontogenesis of children raised in adversity. This study explored the relationship between risk and protective factors, resilience, and youth gambling behavior. More specifically, this study examined the relative contribution of various risk and protective domains in relation to problem gambling behavior and examined whether youth identified as resilient (high risk exposure- high internalized protection) were as likely as those identified as vulnerable (high risk exposure-low internalized protection) to engage in excessive gambling behavior. The sample consisted of 1,273 students ages 12 to 19. The findings demonstrated that risk and protective factors each provide a unique contribution to the prediction model of gambling problems. Resilient and vulnerable youth differed significantly in their self-reported gambling severity. As well, resilient youth were not statistically distinguishable from low-risk exposure groups in terms of their gambling severity. Findings are interpreted with respect to resilience and prevention research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

In Situ Performance Testing of Deteriorating Water Tanks for Durability Assessment

Reports of failure of existing concrete structures due to a lack of durability, rather than a deficiency in structural strength, has made concrete technologists, engineers, and researchers focus research on the parameters influencing durability performance with respect to time. Systematic performance monitoring, with respect to chosen durability parameters of existing concrete structures, will decide the direction of future research in this area. Inferences based on laboratory simulations and testing need to be confirmed by in situ field measurements and studies. In situ condition rating and performance monitoring surveys have been conducted by many researchers, scientists, and professional associations, and reported in literature. Inferences of few such studies are summarized and discussed. Deterioration of concrete structures constructed in recent times is observed at relatively faster rates, and has been mainly attributed to cracking. Cracking is associated with the use of faster-hydrating portland cements with increased fineness and the tricalcium silicate (C3S) content to support the high speed of modern construction. In the present research, a case study of deteriorated water tank structures situated in the semitropical region of India is presented. Some selected parameters—such as concrete cover, carbonation depth, chloride concentration, compressive strength, etc. which influence long term durability of structures—have been measured.     

A new method to estimate parameters of linear compartmental models using artificial neural networks

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models.