Short interpregnancy interval and misoprostol as additive risks for uterine rupture: a case report.

BACKGROUND: Short interpregnancy interval and uterine instrumentation are risk factors for uterine rupture in subsequent pregnancies. Misoprostol as a uterotonic agent is an additive risk factor for rupture of a scarred uterus. CASE: Misoprostol induction for a term stillbirth was complicated by uterine rupture. Risk factors for this uterine rupture might have included interpregnancy intervals of < 7 months, prior uterine instrumentation, and misoprostol as a uterotonic agent. CONCLUSION: Caution and a high index of suspicion are warranted when using misoprostol as a uterotonic agent after a short interpregnancy interval.

A retrospective review of glargine use in pregnancy.

OBJECTIVE: To review the obstetric outcome of 240 diabetic pregnancies maintained on basal glargine insulin. STUDY DESIGN: This is a retrospective review of the medical data from 240 pregnant diabetics who received glargine as a basal insulin. Perinatal outcome was abstracted from August 29, 2001, to December 31, 2007. RESULTS: Mean maternal age was 33 years (SD +/- 5). Seventy-seven percent (184 of 240) of the women were diagnosed with gestational diabetes. The remaining 23% (56 of 240) had a diagnosis of type 2 diabetes. Weekly evaluation of each woman's daily 7x/d fingersticks yielded an individual mean capillary glucose value. These individual mean capillary glucose values were used to calculate a mean value for our sample population. This overall mean capillary glucose value for the 240 parturients was 112 +/- 14.8 mg/dL. The mean neonatal birth weight was 3,142 +/- 606 g. Only 4 neonates had birth weights > 4,000 g (4,365, 4,384, 4,535 and 4,624). None of the neonates were hypoglycemic. CONCLUSION: Prenatal glargine appears to be well tolerated with acceptable perinatal outcome. For well-controlled pregestational diabetics, consideration should be given to continuing glargine during pregnancy.

SEQOPTICS: a protein sequence clustering system.

BACKGROUND: Protein sequence clustering has been widely used as a part of the analysis of protein structure and function. In most cases single linkage or graph-based clustering algorithms have been applied. OPTICS (Ordering Points To Identify the Clustering Structure) is an attractive approach due to its emphasis on visualization of results and support for interactive work, e.g., in choosing parameters. However, OPTICS has not been used, as far as we know, for protein sequence clustering. RESULTS: In this paper, a system of clustering proteins, SEQOPTICS (SEQuence clustering with OPTICS) is demonstrated. The system is implemented with Smith-Waterman as protein distance measurement and OPTICS at its core to perform protein sequence clustering. SEQOPTICS is tested with four data sets from different data sources. Visualization of the sequence clustering structure is demonstrated as well. CONCLUSION: The system was evaluated by comparison with other existing methods. Analysis of the results demonstrates that SEQOPTICS performs better based on some evaluation criteria including Jaccard coefficient, Precision, and Recall. It is a promising protein sequence clustering method with future possible improvement on parallel computing and other protein distance measurements.

Agent computing themes in biologically inspired models of learning and development.

After evaluating general features and attributes of the agent notion, the overlap of features in candidate (attribute) cores, and several less central features, the paper addresses agent and related theory in neuroscience, observing how agent notions have penetrated portions of this field and how the field itself emphasizes and further develops some agent themes via, e.g. schema theory, neural net-artificial intelligence (AI) comparisons, and other research. In remaining sections, models for development of memory strategies in children are presented, illustrating cooperative and competitive neural modeling agents, an active role for a "human agent in the loop," and integrating broadly-based neural network (NN) modeling with other bio-inspired models.

Neural Network Models of Strategy Development in Children.

This paper presents new models of strategy development in young children. Traditional approaches to strategy development: (a) have relied on top-down mechanisms; (b) have not incorporated situational influences; (c) have not been biologically motivated. Neural networks models are described which address the limitations of these traditional approaches. The development of the models was influenced by: (a) Grossberg's modular approach to neural networks; (b) the general theoretical framework of Siegler; (c) empirical research on external representation and memory. Empirically, in situations requiring memory for the arrangement of objects, younger children frequently use simple external memory strategies (e.g. pointing at objects) whereas older children tend toward more advanced strategies (e.g. inter-object orienting) to aid remembering. Computer simulations with the novelty bias model show that strategy novelty and accuracy history can account for selection and evolution of strategies from simple to advanced and for accuracy patterns associated with strategies of different degrees of sophistication. Simulations with a second model, the components model, which overcomes some of the limitations of the first model, demonstrate comparable behavioural evolution using accuracy feedback and selective encoding mechanisms. The models suggest alternatives to the traditional approaches to strategy development, and suggest that the strategy evolution mechanisms involved may apply to a variety of situations. Copyright 1996 Elsevier Science Ltd.