INVESTIGATION OF PATIENT PERCEPTION OF HOSPITAL NOISE AND SOUND LEVEL MEASUREMENTS: BEFORE, DURING, AND AFTER RENOVATIONS OF A HOSPITAL WING

Acoustic conditions in hospitals have been shown to influence a patient’s physical and psychological health. Noise levels in an Omaha, Nebraska, hospital were measured and compared between various times: before, during, and after renovations of a hospital wing. The renovations included cosmetic changes and the installation of new in-room patient audio-visual systems. Sound pressure levels were logged every 10-seconds over a four-day period in three different locations: at the nurses' station, in the hallway, and in a nearby patient’s room. The resulting data were analyzed in terms of the hourly A-weighted equivalent sound pressure levels (

‘Gene shaving’ as a method for identifying distinct sets of genes with similar expression patterns

Background: Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called ‘gene shaving’. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be ‘unsupervised’, that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings. Results: We illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of survival. Conclusions: The gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.