CMV colitis masquerading as colon cancer--an unusual presentation of acquired immunodeficiency syndrome

To determine whether there is any correlation between sudden decrease in barometric pressure and onset of labor, a non-experimental, retrospective study at a 948-bed tertiary care hospital was done. Pregnant patients of 36 weeks gestation or more who presented with spontaneous onset of labor during the 48 hours surrounding the 12 occurrences of significant drop in barometric pressure in 1992 were included in the study. Significantly more occurrences of onset of labor were identified in the 24 hours after a drop in barometric pressure than were identified in the 24 hours prior to the drop in barometric pressure (P < 0.05). Therefore, the overall number of labor onsets increased in the 24 hours following a significant drop in barometric pressure.     

Potential for integration of electrophoretic deposition into electronic device manufacture; demonstrations using silver/palladium

A silver palladium (Ag/Pd) alloy powder is used as an example material to illustrate potential applications of electrophoretic deposition (EPD) in electronics manufacturing, in particular the forming of multilayer devices. The dispersion and deposition of the Ag/Pd powder in acetic acid is characterized. It is found that deposition can be explained by the direct action of electrostatic force on individual particles. Depositions of this Ag/Pd powder are then used to demonstrate: forming of a continuous layer on a rigid substrate; forming of continuous layers in laminated and sintered BaTiO₃ multilayers; and incorporation of patterned depositions into a multilayer by either overcasting the patterned deposition with a particulate slip to form a multicomponent tape, or direct lamination of the patterned deposition to a low temperature co-fired ceramic (LTCC) tape. It is shown that electrically conducting layers can be formed at an average thickness of only two times the diameter of the starting powder. Continuous conductor lines thirty times the average diameter of the starting powder were formed.     

Studies on adsorption equilibrium of xylenes in AEL framework using biased GCMC and energy minimization

The adsorption of xylene isomers in AlPO₄-11 (AEL network) was investigated using biased grand canonical Monte Carlo (GCMC) simulations. Preferential o-xylene adsorption was predicted by the simulations, in agreement with previously reported experimental data. In AlPO₄-11 the selective adsorption behavior comes from the smaller length of the o-xylene molecule along the crystallographic c-axis compared to p-xylene. This is in contrast to AlPO₄-5 and AlPO₄-8, where the ortho-selectivity is caused by the characteristic face-to-face positioning of o-xylene. Energy minimization studies were also performed in a flexible AlPO₄-11 lattice to study the structural changes upon xylene adsorption. The energy minimization study showed that the AlPO₄-11 crystal distorts upon p- and o-xylene adsorption. The distortion mechanism is related to the strong interaction between xylene methyl groups and the sieve oxygen atoms in the O3 position in the wide region of the pore.     

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.     

A behavioral medicine intervention in persons with HIV

This study used a pretest-posttest preexperimental design to examine the effect of a 10-week behavioral medicine support group intervention in a sample of persons with HIV. Using Solomon's psychoneuroimmunologic framework, the 10-week behavioral medicine program focused on the mind/body interaction, the relaxation response, coping with illness, hardiness, and nutrition. Pearson correlation coefficients and t tests were performed on the pre- and postintervention measures of hardiness, social support, immune function, and perceived health status. Results of the study indicated that hardiness (preintervention) and CD4 counts (pre- and postintervention) were significantly correlated with health status; however, CD4 counts decreased over the course of the behavioral medicine program. Implications for nursing and recommendations for further research are discussed.