The SSCs are based on the condition number of the system matrix of a linear imaging model and address invertibility and stability. In the example application of breast CT, the SSCs are used as reference points of full sampling for quantifying the undersampling admitted by reconstruction through TV-minimization. In numerical simulations, factors affecting admissible undersampling are studied. Differences between few-view and few-detector bin reconstruction as well as a relation between

object sparsity and admitted undersampling are quantified.”
“Objective: To test the hypothesis that the first stage of labor will be longer in nulliparous and multiparous women with diabetes compared to non-diabetic counterparts. Methods: A retrospective analysis was performed from 228,668 deliveries

between 2002-2008 from the Consortium SB525334 order of Safe Labor (National Institute of Child Health and Human Development, National Institutes of Health). Patients with spontaneous onset of labor from 37 0/7-41 6/7 weeks gestation were included (71,282) and classified as nulliparous or multiparous. Pregnancies were further subdivided regarding presence of preexisting diabetes (preDM) or gestational diabetes (GDM) and normal controls. Labor curves were created matching for body mass index (BMI) and neonatal birth weight. Statistical analysis was performed on descriptive variables using chi(2) learn more with significance designated as p < 0.05. Results: Among nulliparous patients, there were 118 women with preDM and 475 women with GDM; 25,771 patients served as normal controls. Among multiparous women, there were 311 with preDM, 1,079 with GDM and 43,528 in the control group. Although differences BIIB057 ic50 in dilatation rates were observed in nulliparous and multiparous women with and without diabetes, labor progression

was similar between the subgroups when matched for maternal BMI and birth weight. Conclusions: Labor curves of women with preDM and GDM approximate those of non-diabetics, regardless of BMI, birth weight, or parity.”
“Cancer causes deviations in the distribution of cells, leading to changes in biological structures that they form. Correct localization and characterization of these structures are crucial for accurate cancer diagnosis and grading. In this paper, we introduce an effective hybrid model that employs both structural and statistical pattern recognition techniques to locate and characterize the biological structures in a tissue image for tissue quantification. To this end, this hybrid model defines an attributed graph for a tissue image and a set of query graphs as a reference to the normal biological structure. It then locates key regions that are most similar to a normal biological structure by searching the query graphs over the entire tissue graph.