Deletion of 8p: a report of a child with normal intelligence

The case is presented of a female infant with a distal deletion of 8p (8p23.1 --> pter) whose development was monitored over a 5-year period from 12 months of age. Although previous literature has suggested that 8p deletion is associated with mild to moderate intellectual disability, the child reported here has normal intelligence. Despite initial delays in gross motor and language skills, cognitive development (assessed with the Bayley Scales of Infant Development) and intellectual ability (measured on the Stanford-Binet Intelligence Scale) were within average range. It is argued that the small number of previous case reports may have created a misleading impression of intellectual development in individuals with distal deletions of 8p.

Modern scientific methods and their potential in wastewater science and technology

Application of novel analytical and investigative methods such as fluorescence in situ hybridization, confocal laser scanning microscopy (CLSM), microelectrodes and advanced numerical simulation has led to new insights into micro-and macroscopic processes in bioreactors. However, the question is still open whether or not these new findings and the subsequent gain of knowledge are of significant practical relevance and if so, where and how. To find suitable answers it is necessary for engineers to know what can be expected by applying these modern analytical tools. Similarly, scientists could benefit significantly from an intensive dialogue with engineers in order to find out about practical problems and conditions existing in wastewater treatment systems. In this paper, an attempt is made to help bridge the gap between science and engineering in biological wastewater treatment. We provide an overview of recently developed methods in microbiology and in mathematical modeling and numerical simulation. A questionnaire is presented which may help generate a platform from which further technical and scientific developments can be accomplished. Both the paper and the questionnaire are aimed at encouraging scientists and engineers to enter into an intensive, mutually beneficial dialogue. (C) 2002 Elsevier Science Ltd. All rights reserved.

Neonatal respiratory therapy in the new millennium: Does clinical practice reflect scientific evidence?

Respiratory therapy has historically been considered the primary role of the physiotherapist in neonatal intensive care in Australia. In 2001 a survey was undertaken of all level three neonatal intensive care units in Australia to determine the role of the physiotherapist and of respiratory therapy in clinical practice. It appears that respiratory therapy is provided infrequently, with the number of infants treated per month ranging from 0 to 10 in 15 of the 20 units who provide respiratory therapy, regardless of therapist availability. The median number of respiratory treatments per month during the week was three, and on weekends it was one. Respiratory therapy was carried out by physiotherapists and nurses in 54.6% of units, by physiotherapists only in 36.4% of units, and by nurses only in the remaining 9% of units surveyed. There was also a diminution of the role of respiratory therapy in the extubation of premature infants. A review of the literature shows that overall the use of respiratory therapy reflects current evidence. The question remains whether it is possible to maintain the competency of staff and justify the cost of training in the current healthcare economic climate. It seems probable that the future role of physiotherapists in neonatal intensive care unit may be in the facilitation of optimal neurological development of surviving very low birth weight infants.

Model-based clustering in gene expression microarrays: An application to breast cancer data

In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these. clustering problems. To this end, McLachlan et al. [7] developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based -approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van 't Veer et al. [10]. Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a non-standard problem because the number of genes greatly exceed the number of tissue samples. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data.