Several real-time PCR (rtPCR) quantification techniques are currently used to determine the expression levels of individual genes from rtPCR data in the form of fluorescence intensities. In most of these quantification techniques, it is assumed that the efficiency of rtPCR is constant. Our analysis of rtPCR data shows, however, that even during the exponential phase of rtPCR, the efficiency of the reaction is not constant, but is instead a function of cycle number. In order to understand better the mechanisms belying this behavior, we have developed a mathematical model of the annealing and extension phases of the PCR process. Using the model, we can simulate the PCR process over a series of reaction cycles. The model thus allows us to predict the efficiency of rtPCR at any cycle number, given a set of initial conditions and parameter values, which can mostly be estimated from biophysical data. The model predicts a precipitous decrease in cycle efficiency when the product concentration reaches a sufficient level for template-template reannealing to compete with primer-template annealing; this behavior is consistent with available experimental data. The quantitative understanding of rtPCR provided by this model can allow us to develop more accurate methods to quantify gene expression levels from rtPCR data. ß 2005 Wiley Periodicals, Inc.

The primary detector of breast cancer is the human eye. Radiologists read mammograms by mapping exogenous and endogenous factors, which are based on the image and observer, respectively, into observer-based decisions. These decisions rely on an internal schema that contains a representation of possible malignant and benign findings. Thus, to understand the hits and misses made by the radiologists, it is important to model the interactions between the measurable image-based elements contained in the mammogram and the decisions made. The image-based elements can be of two types, i.e., areas that attracted the visual attention of the radiologist, but did not yield a report, and areas where the radiologist indicated the presence of an abnormal finding. In this way, overt and covert decisions are made when reading a mammogram. In order to model this decision-making process, we use a system that is based upon the processing done by the human visual system, which decomposes the areas under scrutiny in elements of different sizes and orientations. In our system, this decomposition is done using wavelet packets (WPs). Nonlinear features are then extracted from the WP coefficients, and an artificial neural network is trained to recognize the patterns of decisions made by each radiologist. Afterwards, the system is used to predict how the radiologist will respond to visually selected areas in new mammogram cases.

The objective of this study was to measure myelination of frontal lobe changes in infants and young children. Twenty-four cases of infants and children (age range 12-121 months) were evaluated by a quantitative assessment of T2-weighted MR image features. Reliable quantitative changes between white and gray matter correlated with developmental age in a group of children with no neurological findings. Myelination appears to be an increasing exponential function with the greatest rate of change occurring over the first 3 years of life. The quantitative changes observed were in accordance with previous qualitative judgments of myelination development. Children with periventricular leukomalacia (PVL) showed delays in achieving levels of myelination when compared to normal children and adjusted for chronological age. The quantitative measure of myelination development may prove to be useful in assessing the stages of development and helpful in the quantitative descriptions of white matter disorders such as PVL.

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